The Seasonal Cycles in the Distribution of Precipitation

The Seasonal Cycles in the Distribution of Precipitation around Cyclones in theWestern North Pacific and Atlantic

EDMUND K. M. CHANG AND SIWON SONG

ITPA/MSRC, State University of New York at Stony Brook, Stony Brook, New York

(Manuscript received 15 February 2005, in final form 25 July 2005)

ABSTRACT

The seasonal cycles in the distribution of precipitation around the western North Pacific and Atlanticcyclones have been examined by compositing quantitative estimates of the precipitation rate relative tocyclone centers. The precipitation data sources considered include estimates produced by the 40-yr Euro-pean Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) project, the satellite-based daily precipitation estimates produced by the Global Precipitation Climatology Project, and estimatesderived based on observed weather reports contained in the Comprehensive Ocean–Atmosphere Data Set(COADS).

Results from all three datasets suggest that for Pacific cyclones, substantially more precipitation is foundin the warm sector in fall than in winter and less precipitation is found behind the cold front in spring andsummer than in winter. The seasonal cycle for Atlantic cyclones is found to be distinctly different. Thedistribution in precipitation around cyclones in fall and winter are not very different, while in spring andsummer less precipitation is found over much of the cyclone.

The implications for the observed seasonal cycles are discussed. The seasonal cycle for Pacific cyclonessuggests that diabatic contributions to the generation of eddy available potential energy (APE) due to latentheat release should be maximal in fall with a relative minimum in midwinter, while for Atlantic cyclonesdiabatic generation of eddy APE in fall and winter is nearly the same. This is suggested to be one of thefactors that can contribute to the observed midwinter minimum in the Pacific storm track, and the absenceof such a minimum in the Atlantic.

Possible reasons contributing to the differences in the seasonal cycle between the two basins are dis-cussed. Preliminary analyses suggest that differences in static stability, availability of moisture, as well asdynamical forcing may all be contributing factors.

Issues on estimating rates of precipitation based on ship reports are addressed in appendix A. It is arguedthat it may be a good time to recalibrate existing schemes.

1. Introduction

To leading order, the growth and decay of cool sea-son midlatitude baroclinic waves and their associatedcyclones/anticyclones can be understood based on thetheory of baroclinic instability (Charney 1947; Eady1949; Simmons and Hoskins 1978). These waves/cyclones mainly grow by tapping the available potentialenergy (APE) of the pole-to-equator temperature gra-dient. In the Northern Hemisphere (NH), this tempera-ture gradient is strongest during winter and weakest

during summer; hence one might expect baroclinicwaves/cyclones to be most active during winter. Such aseasonal cycle is indeed observed in the Atlantic (Na-kamura 1992).

Over the Pacific, the situation is more complex. Na-kamura (1992) showed that baroclinic wave activity isstrongest during late fall and early spring, with a rela-tive minimum during midwinter (the “midwinter sup-pression”). While this phenomenon can be successfullysimulated by GCM experiments (Christoph et al. 1997;Zhang and Held 1999), the exact physical mecha-nism(s) giving rise to this phenomenon is still not wellunderstood. Zhang and Held (1999) successfully simu-lated the midwinter suppression with a stochastic linearstorm track model, suggesting that it may mainly be dueto changes in the basic flow structure. However, similar

Corresponding author address: Dr. Edmund K. M. Chang,ITPA/MSRC, State University of New York at Stony Brook,Stony Brook, NY 11794-5000.E-mail: [email protected]

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efforts by Whitaker and Sardeshmukh (1998) havebeen unsuccessful. The reasons why these two resultsdiffer are still unknown.

While APE is the main fuel for the growth of baro-clinic waves, it has long been known that latent heatrelease can be another important energy source. Nu-merous studies (e.g., Mak 1982; Emanuel et al. 1987;Gutowski et al. 1992; Davis et al. 1993; Reed et al. 1992,etc.) have shown that the growth rate of baroclinicwaves, as well as the peak intensity of cyclones, arestrongly enhanced by latent heat release due to con-densation in the ascending airstreams around a cyclone.Evaluation of eddy energetics contributions from thedifferent energy transformation terms (e.g., Chang etal. 2002) have shown that, on average, latent heat re-lease acts as a significant source of eddy APE.

Chang (2001) compared the eddy energy budget forthe Pacific storm tracks for October and January, usingboth GCM simulations and reanalysis data. The resultsshow that diabatic generation of eddy APE is muchstronger in October than in January, suggesting that theseasonal cycle in the diabatic generation of APE maybe a contributing factor to the midwinter suppression.Based on analyses of the GCM data, Chang (2001)showed that two factors contribute to the differencesbetween diabatic generation in October and in January.First, the variance in the midtropospheric diabatic heat-ing anomaly is stronger in October. Second, diabaticheating correlates better with temperature perturba-tions in October (note that the diabatic generation termis proportional to the covariance between diabatic heat-ing and temperature perturbations). Chang (2001) hy-pothesized that these could be due to more availablemoisture in October, as well as a larger proportion ofprecipitation in January occurring as convection behindthe cold front, reducing its effectiveness as an eddyenergy source.

In this study, we will examine the seasonal cycle inthe distribution of precipitation around midlatitude cy-clones to see whether there is any evidence supportingthe hypotheses of Chang (2001). We will examine model-generated precipitation using the 40-yr European Cen-tre for Medium-Range Weather Forecasts (ECMWF)Re-Analysis data (ERA-40). However, the precipita-tion estimates in the reanalysis data are produced by6-h forecasts starting from the reanalysis and are notreally analyzed data. As such they are expected to beheavily influenced by model physical parameteriza-tions. Hence, we will also examine satellite precipita-tion estimates, as well as precipitation reported by sur-face ship observations, in an attempt to validate theERA-40 precipitation estimates.

While one of the goals of this study is to check the

hypotheses of Chang (2001), documentation of the sea-sonal cycle of precipitation around cyclones is valuablein its own right. As far as we know, no such documen-tation currently exists in the literature. Moreover, com-parisons of precipitation generated by the reanalysisprojects with independent datasets can show us wheth-er there are systematic biases stemming from the modelphysical parameterizations and can serve as a test ofmodel fidelity. The results shown here can also be usedas benchmarks to assess the structure and variability ofprecipitation generated by GCM simulations.

2. Data and methodology

In this study, we have analyzed 6-hourly ERA-40data from January 1958 to August 2002. Cyclones areidentified based on MSLP analyses, while forecast pre-cipitation fields are composited relative to cyclone cen-ters to document the precipitation climatology aroundcyclones. We have also examined composites of otherquantities and selected fields will be discussed later. AllERA-40 data are available on a 2.5° � 2.5° latitude–longitude grid. Since the quality of reanalyses over theoceans prior to 1979 may not be as good (Kistler et al.2001), the analyses will be split into two periods and themain emphasis will be put on the more recent data.However, as discussed briefly in appendix B, resultsfrom the earlier period are consistent with those fromthe more recent epoch.

Here cyclones are defined as minima in MSLP. Tojust include major cyclones and eliminate weak second-ary cyclones in the vicinity of a major cyclone, a cycloneis only included in the sample if the MSLP at its centeris less than that at all other grid points within a 40° �40° box centered on the cyclone center (i.e., eight gridpoints in all four directions). For western Pacific cy-clones, all such cyclones centered within the area 30°–50°N, 150°E to the date line, are included. For the At-lantic sample, we include cyclones centered within theregion 30°–50°N, 60°–30°W. We have also performedcomposites over smaller regions (35°–45°N, 160°–170°E, and 35°–45°N, 50°–40°W, respectively), and theresults are very similar except that they contain moresmall-scale features that are not statistically significant.Monthly composites are performed, but seasonal aver-ages will be shown.

For precipitation composites, since the ERA-40 dataare 6-h totals starting from the analysis time, at anyinstant that a cyclone is identified, the precipitationduring the preceding 6-h period is averaged with thesubsequent 6-h period before compositing to avoid sys-tematic shifting of the precipitation with respect to thecyclone center due to movement of the cyclone. Hence,

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the precipitation represents a 12-h average centered onthe analysis time.

To validate the seasonal cycle, we have also compos-ited satellite-retrieved precipitation around cyclones.The satellite data used in this study are the 1° daily(1DD) data produced by the Global Precipitation Cli-matology Project (GPCP; see Huffman et al. 1997,2001). Daily GPCP data are available since October1996. To compare with the ERA-40 data, the 1° � 1°satellite data are first interpolated onto a 2.5° � 2.5°grid using a simple algorithm that preserves area aver-ages. Since the satellite data represent daily totals, com-posites are formed based only on cyclone positions at1200 UTC, and these composites are compared to com-posites of the 24-h-averaged precipitation computedfrom the ERA-40 data.

The 1DD GPCP precipitation estimates are not di-rect precipitation measurements, but are retrieved froma combination of IR and microwave radiances. Somecommon deficiencies of satellite-retrieved precipitationhave been discussed in Smith et al. (1998) and Adler etal. (2001). As discussed in the 1DD documentation thataccompanies the data (G. Huffman 2005, personal com-munication), the 1DD intercomparison results are stillbeing developed. Hence, at this point in time it is notexactly clear how accurate this dataset is. Thus weshould mainly treat the comparison between the 1DDdataset and ERA-40 precipitation more qualitativelythan quantitatively. Klepp et al. (2003) have comparedGPCP precipitation over the North Atlantic (over aperiod of 4 months) to products produced using othersatellite retrieval algorithms and used voluntary observ-ing ship (VOS) observations in an attempt to validatethe various products. Their results suggested that mostproducts (including GPCP) perform well for frontaland cyclogenesis precipitation, but most algorithms(again including GPCP), apart from the algorithm fromBauer and Schlussel (1993), fail to recognize convective

rainfall over the cold backside regions of cyclones be-hind cold fronts. We need to keep this possible defi-ciency in mind when we examine GPCP precipitationcomposites.1

Klepp et al. (2003) have used VOS observations tovalidate satellite-retrieved precipitation products. Inthis study, observations from the ComprehensiveOcean–Atmosphere Data Set (COADS: see Woodruffet al. 1987) will be composited to examine the distribu-tion of precipitation around cyclones. Due to a changein the code in reporting present weather introduced in1982 (see discussions in appendix A), which is not com-pletely implemented in the data archival procedure un-til 1985, we will only base our composites on data since1985. In Fig. 1a, the distribution of VOS observationsrelative to western Pacific cyclone centers are shown.Note that, in this and subsequent figures, the geo-graphic locations are for reference only (i.e., the centerof the box in fact represents the center of a cyclone,which can be located anywhere within a 30° � 20° boxdescribed above). In the figure, the distribution of thetotal number of VOS observations (averaged over the12 calendar months) within a 12-h period2 centered onthe time when the cyclone is identified with valid pres-sure, wind, temperature, and present weather observa-tions are shown. We see that over much of the area,apart from the northern and southeastern fringes, thereare more than 500 VOS observations per 2.5° � 2.5°grid box for each monthly composite. In Fig. 1b theaverage number of observations for each monthly com-posite over the Atlantic are shown. There are on aver-

1 Unfortunately, daily precipitation estimates based on theBauer and Schlussel (1993) algorithm are not currently available(C. Klepp 2005, personal communication).

2 As discussed above, in this study, the 12-h average precipita-tion from ERA-40 is composited. Hence, the same time period isused to composite VOS observations.

FIG. 1. Distribution of the average number of VOS observations per monthly composite for (a)western North Pacific and (b) western North Atlantic cyclones.



age fewer observations (in part due to the smaller num-ber of cyclones; see discussions below), especially to-ward the northern part of the domain. In the followingsections, we will show that with these numbers of ob-servations, statistically significant differences betweenthe seasons can be obtained.

Before we proceed further, we would like to firstexamine whether we can obtain a reasonable structureof cyclones based on composites of VOS observations.In Fig. 2a, we show composites of MSLP and 10-m windvectors around western Pacific cyclone centers in Janu-aries based on ERA-40 data. In all, a total of 926 cy-clones were identified and composited. The compositecyclone has a central pressure of just under 980 hPa,with cyclonic flow around its center.

There are two issues in considering composites ofVOS observations. First, ship observations contain spa-tial and temporal gaps, and we need to check whetherthe spatial and temporal distributions of observationsare sufficient to give an unbiased picture. Second is theissue of whether the quality of ship observations is goodenough to produce useful results. The first issue can beassessed by compositing ERA-40 data only over gridboxes where there are ship observations [this will bereferred to as the VOS-sampling (VSAMP) strategy].The results for MSLP and the 10-m wind are shown inFig. 2b.3 Comparing Fig. 2b to 2a, we see that much ofthe cyclone structure can be seen in the VSAMP com-posite. The central pressure of the composite cyclone isnow slightly higher, and the pressure contours are notas smooth. The wind field is also well reproduced.

The issue regarding the quality of VOS observationscan be assessed by comparing the composite of VOSobservations (Fig. 2c) to the VSAMP composite. Com-parison of Figs. 2b and 2c reveals that the structure ofthe pressure distribution is very similar, except that theminimum MSLP in the VOS composite is about 2 hPahigher than that in the VSAMP composite (we are cur-rently investigating the reason behind this bias). Thestructure of the wind fields is also very similar. Overall,comparisons between all three panels in Fig. 2 suggestthat the sampling should be sufficient to provide a moreor less unbiased picture of the large-scale features, andthe quality of VOS observations are good enough (i.e.,the errors do not overwhelm the signal—at least in thecase of MSLP and wind observations) to portray thecyclone structure.

VOS observations do not contain quantitative pre-cipitation measurements. Instead, the reports indicate

whether precipitation is occurring or occurred duringthe recent past. While the reports do contain indica-tions of intensity as well as precipitation type, to quan-titatively compare ship reports to model-generated pre-cipitation estimates one has to translate the differentprecipitation report categories into precipitation rates.

3 Note that for Figs. 2b and 2c, no 12-h averaging is done: com-posite is done only when observations are available at the timewhen the cyclones are identified.

FIG. 2. Composite of MSLP (contours, contour interval 4 hPa,thick dashed contour represents 1000 hPa) and 10-m wind (vec-tors, scale in m s�1 on bottom), for western North Pacific cyclonesin January based on (a) ERA-40 data, (b) VOS-sampled ERA-40data, and (c) VOS data.



Tucker (1961) used quantitative precipitation obser-vations at coastal land stations in Britain to calibrateprecipitation categories in current weather observa-tions. In appendix A, Tucker’s scheme is assessed bycomparing NH ocean precipitation climatology com-puted based on COADS reports and Tucker’s schemeto GPCP and ERA-40 precipitation climatology. Ourresults suggest that Tucker’s scheme underestimatesthe precipitation rate when the precipitation is rain,while it overestimates the rate when the precipitation issnow [consistent with results of Dorman and Bourke(1978) and Klepp et al. (2003)]. Tucker assigned thesame liquid precipitation rate for rain and snow, whichwe think is unreasonable. For example, under the Na-tional Weather Service (NWS) guidelines [(Office ofthe Federal Coordinator for Meteorological Servicesand Supporting Research) OFCM 1995] moderate rainis reported when the rainfall rate is between 0.11 and0.3 in. h�1. If it is snowing at such a rate of water perhour (a very rough 1 to 10 translation between rain andwet snow would suggest 1–3 in. of snow h�1), it wouldmost likely be reported as heavy snow.

We have developed a modification of Tucker’sscheme by partitioning reports of precipitation into rainand snow. Tucker’s precipitation rates for rain are in-creased by a factor (FR: estimated to be 2), and hisrates for snow are decreased by a factor of (FS: equal to0.5). This modified scheme is discussed in more detailsin appendix A.

3. Precipitation around cyclones in the westernNorth Pacific

a. ERA-40

The seasonal cycle in the distribution of precipitationaround western Pacific cyclones, based on ERA-40data, are shown in Fig. 3. A total of 2646 cyclones aresampled for winter [December–February (DJF)], 2677for spring [March–May (MAM)], 1978 for summer[June–August (JJA)], and 1589 for fall [September–November (SON)]. While the total number of cyclonesis comparable for winter and spring, there are signifi-cantly fewer cyclones during summer and fall. To testwhether the structure shown in Fig. 3 is sensitive to thenumber of cyclones, we limit the sampling of cyclonesduring each winter and spring month to the deepesttwo-thirds by imposing a central pressure cutoff crite-rion, such that the total number of cases for all fourseasons becomes more comparable. The results (notshown) are very similar to Fig. 3. Hence, we will con-tinue to show results that include all cases.

The contours shown in Figs. 3a–d show the seasonalcycle of the composite MSLP. We can see that the av-

erage cyclone appears to be deepest in winter. How-ever, the central pressure of cyclones is not necessarilya good indicator of cyclone intensity since the large-scale background pressure distribution has a significantseasonal cycle. If cyclone intensity is quantified in termsof deviation of MSLP from its monthly mean (figuresnot shown), the composite cyclone is deepest in Octo-ber and March (�25 hPa) and slightly weaker in Janu-ary and February (�23 hPa).4 This could be consideredas one of the (many) manifestations of the midwintersuppression.

Examining the distribution of the model-generatedprecipitation (shaded areas), we see that the heaviestprecipitation for all four seasons is located immediatelyto the northeast of the cyclone center. The magnitudeof the maximum is largest during fall, and weakest dur-ing summer. The spatial extent of precipitation is mostextensive during winter, especially to the west of thecyclone. These differences are highlighted in the differ-ent plots shown in Figs. 3e–g. In these panels, theshaded regions represent regions where the seasonaldifferences are significant at the 5% level based on atwo-tailed Student’s t test. Comparing spring to winter(Fig. 3e), we see that there is less precipitation in spring,especially southwest of the cyclone. The reduction inprecipitation is even more pronounced in summer.Comparing fall to winter (Fig. 3g), there is a strongincrease in precipitation near the storm center (up to 9mm day�1, compared to a maximum of 15 mm day�1

during winter) in fall, and a decrease toward the south.Comparing fall to spring, there is less precipitation inspring over much of the cyclone.

To see whether this seasonal cycle is particular to thephysical parameterizations used in the ECMWF re-analysis model, we have also examined precipitationdata for October, January, and April from the NationalCenters for Environmental Prediction–National Centerfor Atmospheric Research (NCEP–NCAR) reanalysis(Kalnay et al. 1996). For the distribution of total pre-cipitation, the results for these three months are nearlyidentical to those computed based on the ERA-40 data.There are some differences in the partition betweenconvective and stratiform precipitation, with theNCEP–NCAR data showing more convective and lessstratiform precipitation (especially in winter) than theERA-40.

b. COADS VOS observations

Before we examine composites based on the VOSobservations, we first examine the VSAMP composites

4 Because of the large number of events composited, this 2-hPadifference is statistically significant at the 5% level.



FIG. 3. Distribution of composite MSLP (contours, contour interval 4 hPa, thick dashedcontour represents 1000 hPa), and rate of precipitation (shading, at 2, 4, 8, 12, and 16 mmday�1), for western North Pacific cyclones in (a) winter, (b) spring, (c) summer, and (d) fall,computed based on ERA-40 data. Differences in rate of precipitation (contours, contourinterval 1 mm day�1, zero contour omitted) between (e) spring and winter, (f) summer andwinter, and (g) fall and winter. Shaded regions denote statistical significance at the 5% level.



of ERA-40 data. These are shown in Fig. 4. Comparingthe pressure composites, it is apparent that the VSAMPcomposite cyclone is not as deep, with the central pres-sure being about 4 hPa higher than that shown in Fig. 3in winter and 3 hPa higher in fall. Despite the biases,the seasonal cycle of deepest cyclones in winter (interms of total pressure) is still clearly evident.

Comparing the precipitation pattern, while there aresome apparent distortions of the patterns due to sam-pling, the patterns are still very similar to those shownin Fig. 3. The seasonal cycle is also quite similar exceptthat, during spring and summer, the area to the north-east of the cyclone center over which the precipitationexceeds that in winter is more extensive in the VSAMPcomposites. Comparing Figs. 4e–g to 3e–g, quantita-tively, the spatial correlations are 0.93, 0.90, and 0.94,respectively. Figure 4 shows that VOS sampling shouldnot introduce serious biases to the seasonal cycle.

Composites based on VOS observations are shown inFig. 5. These should be compared to the VSAMP ERA-40 composites shown in Fig. 4. Comparing the seasonalcycle in MSLP (contours in Figs. 5a–d), we see that theseasonal cycle of deepest cyclones in winter and weak-est in summer is again seen in the VOS composites.Similar to what is shown in Fig. 1, the central pressureof the composite cyclones in the VOS composites areslightly higher than those in the VSAMP composites by1–2 hPa.

Using our modified Tucker scheme (details pre-sented in appendix A), the seasonal cycle in the com-posite distribution of precipitation around westernNorth Pacific cyclones computed based on VOS obser-vations is shown in Fig. 5 (shades in Figs. 5a–d). Com-paring Figs. 5a–d to 4a–d, it is apparent that the pre-cipitation rate in the warm sector is a bit higher in theERA-40 VSAMP composites, while in the cold sectorwest of the cyclones, the COADS composites showmore widespread precipitation. However, consideringthe uncertainties involved in transferring VOS obser-vations to precipitation rate, the agreement betweenthe two sets of figures is remarkable.

Comparing the seasonal differences shown in Figs.4e–g and 5e–g, they are again very similar. Quantita-tively speaking, the spatial correlations between Figs.5e–g and 4e–g are 0.79, 0.86, and 0.83, respectively.Overall, the distribution of precipitation around cy-clones derived based on the VOS observations confirmthat precipitation over and to the northeast of the cy-clone center is much higher in fall than in winter andthat precipitation over the cold sector to the southwestof the cyclone center is significantly lower in fall andspring than in winter. Both of these are consistent with

the composites based on ERA-40. In appendix A, wewill show that the results computed based on theschemes suggested by Dorman and Bourke (1978) andKlepp et al. (2003) are consistent with those shown inFig. 5, showing that the results are not very sensitive tothe scheme used in translating VOS observations intoprecipitation rates.

c. GPCP precipitation

Next, we will examine the precipitation distributionbased on GPCP data. As discussed above, GPCP andERA-40 overlap only during October 1996–August2002. In addition, the GPCP data represents daily to-tals. Hence we will only composite based on cyclonepositions at 1200 UTC, and combine the 6-hourlyERA-40 precipitation data into daily totals before com-positing to be consistent with the GPCP data.

First, we will examine the seasonal cycle based onthis subset of ERA-40 data. Since the overall precipi-tation distribution around the cyclones is quite similarto that shown in Figs. 3a–d, we will only show thedifferences between winter and the other seasons.The composite seasonal cycle is shown in Figs. 6a–c.Comparing Fig. 6 to Fig. 3, we find a very similar sea-sonal cycle. The spatial correlations between Figs. 3e–gand 6a–c are 0.86, 0.94, and 0.82, respectively. Thisshows that, at least according to the ERA-40 data, the1996–2002 period is representative of the entire periodsince 1985. Smoothing the precipitation data to dailytotals also does not significantly alter the seasonalcycle.

For comparison, the composites based on GPCP es-timates are shown in Figs. 6d–f. Comparing the precipi-tation distribution for individual seasons to the ERA-40data (not shown), we can see that the peaks in theGPCP composites show slightly weaker maxima nearthe cyclone center, and precipitation tends to cover abroader area, especially in spring and summer. The dif-ferences between winter and the other seasons (Figs.6d–f) are broadly consistent with the ERA-40 data, butthe agreement is not as good as that between ERA-40and VOS observations (Figs. 4 and 5). Nevertheless,GPCP data are still clearly consistent with maximumprecipitation near the cyclone center in fall, as well asless precipitation west of the cyclone in spring and sum-mer.

4. Precipitation around North Atlantic cyclones

The seasonal cycle in the distribution of precipitationaround western North Atlantic cyclones are examined



FIG. 4. As in Fig. 3 except for VOS-sampled ERA-40 data.



FIG. 5. As in Fig. 3 except for VOS data. The precipitation rate is computed from amodification of the scheme of Tucker (1961) described in appendix A.



using the same compositing procedure employed forPacific cyclones. A total of 1217 cyclones are compos-ited for winter, 1733 for spring, 731 for summer, and1062 for fall. The composites for MSLP and 12-h aver-age rainfall rate, based on ERA-40 data, are shown inFig. 7. Compared to the composites of western NorthPacific cyclones shown in Fig. 3, we see that, in terms oftotal MSLP, the western North Atlantic cyclones arenot as deep except perhaps in summer. Nevertheless,they do show a similar seasonal cycle in being deepestin winter and weakest in summer. If cyclone intensity is

measured in terms of deviations from monthly mean,the composite Atlantic cyclone is also deepest in Janu-ary and February, with a minimum MSLP perturbationof �26 hPa. The MSLP perturbations for compositecyclones in October and April are �20 and �19 hPa,respectively.

Inspecting the distribution of precipitation, the sea-sonal cycle, as indicated by the differences betweenwinter and the other seasons (Figs. 7e–g), is distinctlydifferent from those shown in Figs. 3e–g. Over the At-lantic, precipitation around cyclones in winter is clearly

FIG. 6. (a)–(c) As in Figs. 3d–f except for ERA-40 data sampled based on GPCP dataavailability and (d)–(f) except for GPCP precipitation data.



FIG. 7. As in Fig. 3 except for western North Atlantic cyclones.



heavier than that in spring and summer but, unlike whathappens over the Pacific, the precipitation deficit inthese seasons bears strong resemblance to the pre-cipitation distribution itself (cf. Figs. 7e,f to 7a), sug-gesting simply an overall reduction. In fall (Fig. 7g),there is only a slight increase in precipitation near thecyclone center instead of a strong increase. Thus, over theAtlantic, precipitation is heaviest during fall and winter,and lightest in summer. Visually, the patterns shown inFigs. 3a–d and 7a–d clearly look different. Statisticallyspeaking, if we shift the panels to a common grid and

subtract them, the differences between the panels (notshown) show clearly statistically significant patterns.5

In Fig. 8, the results based on VOS observations areshown. Figures 8a–c show the differences between win-

5 Overall, on a grid point by grid point basis, the differencesbetween Figs. 3e and 7e are significant at the 1% confidence levelover 38% of the 221 grid points (53% for Figs. 3f and 7f, and 35%for Figs. 3g and 7g). Such high percentages of statistically signifi-cant grid points clearly pass any field significance tests (e.g.,Livezey and Chen 1983).

FIG. 8. (a)–(c) As in Figs. 7d–f except for VOS-sampled ERA-40 data and (d)–(f) exceptfor VOS data.



ter and the other seasons based on VSAMP ERA-40data. Comparing Figs. 8a–c to 7e–g, VOS sampling againdoes not appear to be an issue. The composites based onVOS observations, with the precipitation rate estimatedbased on our modification of Tucker’s scheme, areshown in Figs. 8d–f. These panels clearly are consistentwith Figs. 8a–c. The pattern correlations between Figs.8a–c and 8d–f are 0.84, 0.81, and 0.63, respectively.Comparing to their Pacific counterparts (Figs. 5e–g),the seasonal differences are also clearly different.

The composites based on ERA-40 data using GPCPsampling is shown in Figs. 9a–c. These are again con-sistent with those obtained using the ERA-40 databased on the entire period (Figs. 7e–g). The GPCPcomposites are shown in Figs. 9d–f. The patterns shownin Figs. 9a–c and 9d–f correlate at 0.87, 0.84, and 0.66,respectively. Clearly, Figs. 8 and 9 show that the resultsbased on VOS observations, as well as those usingGPCP precipitation estimates, are largely consistentwith the results obtained using the ERA-40 data.

FIG. 9. (a)–(c) As in Figs. 7d–f except for ERA-40 data sampled based on GPCP dataavailability and (d)–(f) except for GPCP precipitation data.



5. Discussion

a. Histograms of the precipitation rate

In the preceding section, we have displayed the spa-tial distribution of mean precipitation around cyclones.It is well known that the temporal distribution of pre-cipitation is highly non-Gaussian, hence it is possiblethat the differences between the seasons could be domi-nated by just a few particularly heavy events. Here wewill examine histograms of the precipitation rate to seewhether this is the case. Since we have shown that thedistribution of ERA-40-generated precipitation is quiterealistic, we will compute histograms from this dataset.

When we examined Fig. 3, we saw that one of themajor differences between winter and fall Pacific cy-clones is that the precipitation immediately to thenortheast of the cyclone is much heavier in fall. Here,this difference is examined further. The 12-h averageddaily precipitation rate at each grid point within thearea �0° to �5° latitude and longitude, relative to thecyclone center, is binned into 5 mm day�1 bins from 0–5mm day�1 up to 45–50 mm day�1, with the final bincontaining the rest. The percentage distribution foreach bin, for fall (thin gray bar) and winter (unfilledbar), is shown in Fig. 10. For reference, the mean pre-cipitation rate within the area is 13.1 mm day�1 in win-ter and 19.3 mm day�1 in fall.

Comparing the two distributions, we see that bothare clearly non-Gaussian, with highest frequency in thelowest bin. The winter distribution has higher fre-quency for all bins below 15 mm day�1 and lower fre-quency for all bins above 20 mm day�1. Hence, Fig. 10suggests that the difference between fall and winter Pa-cific cyclones is probably not due to a few high precipi-tation cases, but is instead due to an overall change inthe shape of the frequency distribution of precipitationrate.

b. Preliminary thoughts concerning seasonal andbasin differences

In section 4, we have seen that there are substantialseasonal as well as geographical differences in the dis-tribution of precipitation around cyclones. We have ex-amined composites of a number of other quantities inorder to gain insights into what factors might have con-tributed to such differences. Some preliminary resultsare presented here.

One of the differences we found was that the pre-cipitation west of the cyclone is much more extensivefor Pacific storms in winter than in other seasons (Fig.3). In the ERA-40 data, the total precipitation is parti-tioned between convective and stratiform precipitation,and examination of composites of these two compo-

nents (not shown) suggests that most of the differencesare due to convective precipitation. In Fig. 11, we showthe composite temperature difference between 500 and1000 hPa around cyclones. Note that larger tempera-ture decrease from 1000 to 500 hPa implies weakerstatic stability. Comparing winter to spring and fall(Figs. 11a–c), we see that the temperature difference ismuch larger in winter storms, with the weak stabilityregion extending toward the west of the storm. Plots ofthe convective precipitation composites (not shown)show that the location of the convective precipitation ineach season is well correlated with the location of mini-mum static stability.

Comparing Atlantic (Fig. 11d) and Pacific (Fig. 11a)winter storms, it is clear that the stability to the west ofthe cyclone is weaker in the Pacific. This is mainly dueto the fact that in winter, mid- to-upper-tropospherictemperature over East Asia is significantly colder thanthat over northeastern North America. Figure 11 sug-gests that the difference in static stability could be onereason why precipitation over the cold air west of thecyclone is most extensive for Pacific winter cyclones.

Another difference we noticed was that the precipi-tation around the cyclones in spring is lighter than thatin fall in both the Atlantic (Fig. 7) and the Pacific (Fig.3). When we examine the temperature composites at700 hPa (Fig. 12), we find that the temperature gradi-ents around the cyclones are about the same in springand fall. However, in both basins the temperature in fallis about 3 K warmer than that in spring. We also ex-amined composites of total column water vapor (notshown). Consistent with the warmer temperature, the

FIG. 10. Histograms showing frequency (ordinate, in %) of theprecipitation rate (abscissa, in mm day�1) in a 5° � 5° area im-mediately to the northeast of the Pacific cyclones in winter (un-filled bars) and fall (thin gray bars). The rightmost bars representfrequency of over 50 mm day�1.



total column water vapor in fall is significantly higher.We speculate that the larger amount of available mois-ture in fall, coupled with similar dynamical forcing be-tween spring and fall (indicated by similar temperaturegradients), may be a reason why precipitation aroundcyclones is heavier in fall than in spring.

Comparing Pacific and Atlantic cyclones, Fig. 12 sug-gests that dynamical forcing is similar between the twobasins in winter, while temperature (as well as avail-ability of moisture) is higher in the Atlantic, consistentwith more precipitation around Atlantic cyclones (ex-cept west of the cyclones as discussed above). However,in spring and fall, the temperature gradients aroundPacific cyclones are clearly stronger than those over theAtlantic. Hence, we expect dynamical forcing to bestronger in the Pacific than in the Atlantic during springand fall. This may be a reason why there is more pre-cipitation around Pacific cyclones than around Atlanticcyclones during these seasons.

c. Implications on diabatic generation of eddy APE

The seasonal cycle seen in the distribution of precipi-tation has important dynamical implications. Precipita-

tion is preceded by condensation, which implies latentheat release somewhere in the troposphere. Eddy APEgeneration is proportional to the temporal covarianceof heating and temperature anomalies (see, e.g., Chang2001). If positive (negative) latent heat anomalies occurwithin regions where the temperature anomaly is warm(cold), eddy APE is generated. In Fig. 13a, the com-posite temperature anomaly at the 700-hPa levelaround Pacific cyclones, averaged over all months, isshown. A positive temperature anomaly is seen eastand northeast of the cyclone center, while a negativeanomaly can be seen to the southwest. While the plot isfor the 700-hPa level, this pattern holds from near thesurface up to the 400-hPa level.

The annual mean precipitation anomaly around Pa-cific cyclones is shown in Fig. 13b. Corresponding to thepeak in precipitation to the northeast of the cyclonecenter, there is a peak in the precipitation anomaly.West and southwest of the cyclone, where precipitationis lighter (see Fig. 3), there is a negative anomaly. Com-paring Figs. 13a and 13b, it is apparent that APE isgenerated nearly everywhere around the cyclone,within both warm and cold anomalies.

FIG. 11. Composites of temperature differences between 500 and 1000 hPa around cyclonesfor (a) winter, (b) spring, (c) fall in the Pacific, and (d) winter in the Atlantic. Contour intervalis 2 K.



FIG. 12. Composites of temperature at 700 hPa around cyclones for the western NorthPacific cyclones in (a) winter, (b) spring, (c) summer, (d) fall, and western North Atlanticcyclones in (e) winter, (f) spring, (g) summer, and (h) fall. Contour intervals are 3 K.



As discussed above, our results show that in the Pa-cific, there is more precipitation over the warmanomaly in fall than in winter. Hence, the positive pre-cipitation anomaly over the warm air is stronger in fallthan in winter. Comparing spring and winter, there isless precipitation over the cold air in spring. This im-plies that the negative precipitation anomaly in winterover the cold air is not as negative as that in spring. Toquantify these differences, we have computed the sea-sonal variations in the precipitation anomaly aroundcyclones within the cold and warm anomalies. Theformer is computed as the average precipitationanomaly in the region between �10° and �5° latitudeand �20° to �5° longitude relative to the cyclone cen-ter, while the latter is computed in the area �5° to �10°latitude and �0° to �15° longitude. The results areshown in Fig. 14.

The precipitation anomaly within the warm anomalyis shown in Fig. 14a. We see that for Pacific cyclones(solid line) this quantity clearly maximizes in fall, has arelative minimum in winter, increases slightly in spring,and has another minimum in summer. On the other hand,the precipitation anomaly in the cold air (Fig. 14b) ismost negative in fall and spring, and is less negative insummer and winter. Together Figs. 14a and 14b suggestthat diabatic generation of APE due to precipitationshould be largest in fall, followed by spring, with gen-eration in winter less than that in the transition seasons.

In contrast, over the Atlantic, the precipitationanomaly in the warm air (Fig. 14a, dotted line) peaks inNovember and January, with the fall and winter aver-ages being quite similar and the anomaly in spring be-ing substantially weaker. Over the cold anomaly (Fig.14b), the negative anomaly is relatively constant from

fall through spring. Hence, in the Atlantic, Fig. 14 sug-gests that diabatic generation of APE should be similarin fall and winter and weaker in spring and summer.

Chang (2001) found that, in terms of energetics, overthe Pacific the seasonal differences in moist heatingcontribute to enhanced diabatic generation of APE inOctober over that in January. However, Chang (2001)did not find the same effects over the Atlantic. Ourresults here suggest that this could be due to differences

FIG. 14. (a) Seasonal variation in the precipitation rate anomaly(mm day�1) in the warm sector around Pacific (solid line) andAtlantic cyclones (dotted line). (b) As in (a) except for the coldsector. See text for definition of warm and cold sectors.

FIG. 13. Composites of (a) temperature anomalies at 700 hPa (contour interval 0.5 K), and (b) anomalies in therate of precipitation (contour interval 1 mm day�1) for western North Pacific cyclones averaged over the 12months.



between the seasonal cycles in the distribution of pre-cipitation around Pacific and Atlantic cyclones.

We are not claiming that this factor alone can explainthe midwinter suppression. There are other factors thatmay also be important. A case in point is that our re-sults above suggest that diabatic generation of eddyAPE should be stronger in fall than in spring in bothbasins. Indeed, the Atlantic storm track is slightly stron-ger in fall than in spring (see, e.g., Fig. 4b in Chang etal. 2002), consistent with the difference in diabatic ef-fects. However, the Pacific storm track activity in falland spring is quite similar. We have examined a num-ber of other composites and have found indications thatthe upstream perturbations that seed the Pacific stormtrack may be stronger in spring than in fall—the differ-ence in upstream seeding could potentially counteractthe difference in diabatic effects. However, these re-sults are still preliminary and further work needs to bedone to confirm them. Meanwhile, the results of Harnikand Chang (2004) and Nakamura et al. (2002) showthat changes in the midwinter basic flow structureclearly influence the degree of the midwinter suppres-sion, with the suppression being most pronounced dur-ing the years when the midwinter jet is stronger andnarrower (or when the winter monsoon is stronger) andbarely observable during the years when the midwinterjet is weaker and broader. Hence, changes in the jetstructure between midwinter and spring/fall could beanother contributing factor.6 However, the results ofWhitaker and Sardeshmukh (1998) and Harnik andChang (2004) suggest that changes in the basic flowalone cannot fully explain the midwinter suppression.7

We believe that the midwinter suppression can only befully explained by a combination of effects related tochanges in basic-state structure, changes in diabatic ef-fects, as well as other factors such as changes in upstreamseeding and tropical SST distributions (Orlanski 2005).

6. Summary and conclusions

Based on compositing precipitation relative to cy-clone centers, our results show that over the western

North Pacific, there is significantly more precipitationimmediately to the northeast of cyclone centers in fallthan in winter and less precipitation southwest of cy-clone centers in spring than in winter. Similar resultsare obtained based on ERA-40 data, COADS VOSobservations, and GPCP daily rainfall estimates.

Our results also show a distinctly different seasonalcycle for cyclones over the western North Atlantic.Over the Atlantic, the differences between fall and win-ter precipitation are much smaller than those found forPacific cyclones. In spring, there is less precipitationbasically everywhere around the cyclone instead ofmainly to the southwest. Again, similar differences areseen in all three precipitation datasets.

At present, the reasons behind the observed seasonalcycle, as well as the differences between the ocean ba-sins, are not completely understood. Our preliminaryinvestigations suggest that factors including differencesin the distribution of static stability, availability of mois-ture, and dynamical forcing, could all contribute to theobserved differences in precipitation distribution. Fur-ther studies are needed to substantiate these possibili-ties.

In section 5c, we discussed the possibility that theseasonal cycle in the distribution of precipitationaround cyclones could be a factor contributing to theobserved midwinter suppression of the Pacific stormtrack activity, as well as the lack of such a suppressionover the Atlantic. The different seasonal cycles of pre-cipitation distribution have other interesting implica-tions. Theories for moist baroclinic instability (e.g.,Emanuel et al. 1987; Fantini 1995) basically assumesaturation in the warm sector of cyclones, but dry dy-namics apply in the subsiding cold sector. Our resultsshow that a significant fraction of precipitation occursin the cold sector in winter, and this fraction changesover the season and is different over different oceanbasins. How these effects can be incorporated intotheories is not clear. The need for a theory of moistbaroclinic instability is not purely academic since sta-bility studies of the observed winter mean flow (e.g.,Hall and Sardeshmukh 1998) have shown that the ob-served basic state is nearly neutral to dry baroclinicinstability, suggesting that latent heat release must playa role in the growth of baroclinic waves. On a broaderperspective, understanding the role of moist dynamicsin the determination of cyclone statistics could also helpus to understand how such statistics might change un-der global warming scenarios (Held 1993).

Quantitatively speaking, our results are based mainlyon model-generated precipitation estimates producedalongside the ERA-40 reanalysis data, though compari-sons with NCEP reanalysis precipitation data suggest

6 However, the variations in jet structure could also be relatedto latent heat release by eddies as well (Hoskins and Valdes 1990).

7 Even the results of Zhang and Held (1999)—that the midwin-ter suppression can be simulated using a dry stochastic storm trackmodel—do not necessarily negate our results here. The ability tosimulate the seasonal cycle based just on changes in the basic-statestructure does not rule out the possibility that other factors canalso contribute. In addition, the agreement between modeled re-sults and observation could have been due to a cancellation oferrors, as their formulation of the stochastic storm track model isclearly far from perfect (see Delsole and Hou 1999).



that these estimates are not overly sensitive to themodel used. The verification using COADS VOS ob-servations and GPCP precipitation estimates should beregarded as more qualitative than quantitative. VOSreports only contain indications of the intensity of pre-cipitation. While we have been able to translate thosereports into estimates of precipitation rates using amodification of the scheme of Tucker (1961), our modi-fication is more or less ad hoc, and the excellent agree-ment between our results based on VOS and ERA-40data should not be taken too seriously. Nevertheless,some of the modifications we applied (e.g., increasingthe liquid rain rate given by Tucker) do agree qualita-tively with the results of Klepp et al. (2003) and Dor-man and Bourke (1978). With the availability of long-term precipitation observations, it may be worthwhileto rederive Tucker’s calibration from scratch (as sug-gested by Elliott and Reed 1979) using a much moreextensive dataset.

The agreement between the results based on ERA-40- and GPCP-derived rainfall should also be treatedwith caution. Firstly, quantitative verification of satel-lite-derived precipitation is difficult due to limitationsof the validation data in terms of both coverage andquality (Adler et al. 2001). In addition, as mentionedabove, the results of Klepp et al. (2003) suggested thatGPCP precipitation retrievals may contain deficienciesin terms of underestimating the precipitation rate in thecold sector. While Klepp et al. (2003) suggested thatretrievals based on the Bauer and Schlussel (1993)scheme may be more accurate, their results are basedon comparisons with a limited number (101) of VOSobservations over a short duration. In addition, the newVOS rain-rate translation scheme developed by Kleppet al. (2003) was derived based on comparisons withsatellite-retrieved rain rates, so their verification usingVOS data is not really based on entirely independentdata. Moreover, results shown in appendix A suggestthat the precipitation rates suggested by Klepp et al.(2003) may well be overestimations. Thus, before a newand complete scheme that translates VOS observationsto precipitation rate based on long-term quantitativeprecipitation data has been derived, we cannot really beconfident about using VOS data to calibrate satellite-retrieved precipitation.

Acknowledgments. The ECMWF reanalysis data areobtained from the ECMWF data server. The COADSdata are obtained from the NCAR data archives. GPCPprecipitation data are obtained from NASA (see onlineat http://rsd.gsfc.nasa.gov/). The authors thank these in-stitutions for making the data available. Commentsfrom Dr. Mankin Mak and two anonymous reviewers

have helped to clarify some of the discussions. Wewould also like to thank George Huffman for providinginformation regarding the GPCP data. This research issupported by NSF Grant ATM0296076 and NOAAGrant NA16GP2540.

APPENDIX A

Translating VOS-Observed Weather Codes to thePrecipitation Rate

Weather is reported by VOS based on a two-digitcode (ww from 00 to 99) defined by the World Meteo-rological Organization (WMO 1995). Out of these 100codes, 66 are associated with some form of precipita-tion. Descriptions of these 66 codes can be found inPetty (1995).

Until 1981, all observed weather (including no sig-nificant weather) is reported in the ww group. Starting1982, WMO implemented a new code (the IX group inCOADS) to allow the ww group to be left out from areport when there is no significant weather. Unfortu-nately this code was not properly implemented into thedata archival system until 1985. Hence if ww is missingduring 1982–84, it is not clear whether a weather ob-servation was not taken or that no significant weatherwas observed. This ambiguity could potentially impactthe frequency of significant weather computed basedon COADS data. Petty (1995) addressed this potentialproblem by treating missing ww as being an indicationof no significant weather if a valid cloud amount isreported. In this study, an alternative strategy is used:VOS weather data from 1982 to 1984 are not includedin the analyses. The main results are based on datasince 1985, and data from 1958 to 1981 are separatelyanalyzed to test the robustness of the results (see ap-pendix B).

The compositing proceeds as follows: For each gridbox, if a ship report exists, ww and IX reports are onlyconsidered if they are accompanied by valid pressure,wind, and temperature reports, and the report is notclassified as a duplicate by COADS. At each grid box,an array with 101 cells is stored. Each cell (00–99) isincremented by 1 when a ship observation having thatcode is reported in that grid box. The 101st cell will beincremented by 1 for every valid ww observation, aswell as when the IX code indicates that ww is not re-ported due to no significant weather. With this tabula-tion, the frequency of occurrence of every ww code atall grid boxes can be computed.

We have applied the technique to obtain a global2.5° � 2.5° climatology of frequency of precipitation,



similar to that obtained by Petty (1995). Our results areconsistent with those of Petty (see Figs. 11–13 of Petty1995) and are not shown here.

To quantitatively compare the VOS results to thosebased on ERA-40, we need to translate precipitationfrequency to an average precipitation rate. Tucker(1961) developed such a scheme by comparing ww re-ports to quantitative precipitation observations at anumber of British coastal stations. Since the number ofww codes involved is large, there are not enough ob-servations to determine the equivalence of every code.Hence Tucker (1961) developed a parameterizationbased on three parameters: the precipitation rate forcontinuous light, moderate, and heavy precipitation, re-spectively. All other categories are expressed as somelinear combinations of these three rates. For the threebasic rates, Tucker used the values 0.62, 1.89, and 2.71mm h�1, respectively. The numerical values for Tuck-er’s scheme are shown in Table A1. Note that in Tuck-er’s original table, the rates for codes 54 and 55 are toohigh because of typos (Tucker 1962).

Examining Table A1, we can see that Tucker (1961)assigned the same rate of precipitation for rain (ww

FIG. A1. (a) Average precipitation rate (contour interval is 1 mm day�1) over the western North Pacific in January based on GPCPmonthly data from 1985 to 2001. Regions where the precipitation rate is over 5 mm day�1 are shaded; (b) as in (a) except computedbased on VOS-observed precipitation frequency and converted to precipitation rate using the scheme of Tucker (1961); (c) as in (b)except only for rain; (d) as in (b) except only for snow.

TABLE A1. Precipitation rate (mm h�1) for ww codes 50–99based on Tucker (1961). Rates for snow categories are italicized.Rates in parentheses are calibrations due to Klepp et al. (2003).

ww 50 60 70 80 90

0 0(0.3)

0.3(1.1–2.0)

0.3 0.3(1.1–2.0)

2.3(�5.0)

1 0.3(0.3)

0.6(1.1–2.0)

0.6(1.1–2.0)

0.9(3.1–4.9)

0.6

2 0.3(0.4–0.6)

0.9(2.1–3.0)

0.9(1.1–2.0)

1.4(�5.0)

2.3

3 0.6(0.4–0.6)

1.9(2.1–3.0)

1.9 0.3(1.1–2.0)

0.6

4 0.6 1.4 1.4 1.1(2.1–3.0)

2.3

5 1.2(0.7–1.0)

2.7 2.7 0.3 1.3

6 0.3(0.7–1.0)

0.6 0 1.1(2.1–3.0)

1.3

7 0.9(0.7–1.0)

2.3 0 0.3(1.1–2.0)

2.7

8 0.3 0.6 0(0.3)

2.3(2.1–3.0)

0(1.1–2.0)

9 0.9 2.3 0 0.3(3.1–4.9)

2.7



equals 60–65) and snow (70–75—all reports of snowhave been underlined in Table A1). Based on precipi-tation reports from two Swedish stations, Andersson(1962) suggested that Tucker’s precipitation rate forsnow may have been too high. Dorman and Bourke(1978, hereafter DB78) found that Tucker’s precipita-tion rate appears too low at low latitudes and too highat high latitudes. DB78 developed a correction bymodifying Tucker’s rates by a factor that depends qua-dratically on the mean temperature. For the annualmean, this multiplicative factor goes from 0.34 at tem-peratures near �5°C, to 1.0 at 6.7°C, up to 5.09 at 30°C.

While the climatologies produced by Dorman andBourke (1978, 1979, 1981) appear reasonable, even incomparison to modern climatologies (e.g., Adler et al.2003; Legates and Willmott 1990), Elliott and Reed(1979) pointed out that a multiplication factor that de-pends purely on temperature is unrealistic. For ex-ample, a multiplicative factor of 5 at 30°C would meanthat light and moderate rain will be assigned rates of 3.1and 9.5 mm h�1, respectively, which are clearly toohigh. Elliott and Reed (1979) suggested that Tucker’sscheme should be completely recalibrated.

Klepp et al. (2003) used VOS observations from 101ships to validate satellite-retrieved rainfall over the re-gion behind a cold front. They calibrated the VOS ob-servations using satellite-retrieved rainfall over othersectors around the cyclone, and their calibration isshown in parentheses below Tucker’s precipitation ratein Table A1. Their results suggested that Tucker’s pre-cipitation rates for rain are generally too low, consistentwith the discussions above. However, due to the smallnumber of VOS observations considered by Klepp etal., not all precipitation categories (especially those re-lated to snow) are covered in their calibration.

We have computed a monthly precipitation climatol-ogy for 1985–2001 by applying Tucker’s scheme (TableA1) to the observed frequency based on COADS dataat each 2.5° � 2.5° grid box. The results for Januaryover the western North Pacific are shown in Fig. A1b.Comparing to the climatology computed based onGCPC monthly data (Fig. A1a), we observe that theCOADS climatology, based on Tucker’s originalscheme, has too little precipitation between 30° and40°N and too much precipitation at higher latitudes.We can separate the precipitation into rain and snow.The results are shown in Figs. A1c,d. It is clear that,consistent with the discussions above, the precipitationrates based on Tucker’s scheme for rain are too low,while those for snow are too high.

A complete recalibration of Tucker’s scheme is be-yond the scope of this work. Instead, we decided just to

modify Tucker’s scheme by assigning separate precipi-tation rates to rain and snow. Two multiplicative fac-tors, FR and FS, are defined for rain and snow, respec-tively. These two factors are estimated by requiring thatthe sum of the rms error and mean absolute bias formonthly climatology, computed using this scheme, ascompared to climatologies based on GPCP and ERA-40 data, be minimized over the western North Pacificand Atlantic. Best results are obtained when FR equals2 and FS equals 0.5. Roughly speaking, these factors are

FIG. A2. (a) As in Fig. A1b except computed based on ourmodification of Tucker’s scheme; (b) as in Fig. A1b except com-puted based on the scheme of DB78; (c) as in Fig. A1b exceptcomputed based on the scheme of Klepp et al. (2003).



consistent with the results of Klepp et al. (2003) andDB78 discussed above. The precipitation climatologycomputed based on this set of parameters for January isshown in Fig. A2a. For comparison, the climatologycomputed using the scheme of DB78 is shown in Fig.A2b, and that based on the scheme suggested by Kleppet al. (2003) is shown in Fig. A2c. In generating Fig.A2c, the missing categories in Klepp et al.’s table havebeen filled in by our modification to Tucker’s scheme,but most of the precipitation actually comes from thecategories that have been defined by Klepp et al. Com-

paring Figs. A2c to A1a, it appears that the climatologybased on Klepp et al.’s scheme has too much precipi-tation. Comparing our results (Fig. A2a) to those basedon the scheme by DB78 (Fig. A2b), our fit to the sea-sonal cycle over the western North Pacific and Atlanticusing just two factors (FR and FS) that are kept con-stant throughout the year is actually slightly better (interms of rms differences and absolute bias, as comparedto the GPCP and ERA-40 climatologies) than thatcomputed base on DB78’s scheme using seasonallyvarying parameters. These results suggest that a recali-

FIG. A3. (a)–(c) As in Figs. 5e–g except computed based on the scheme of DB78; (d)–(f)as in Figs. 5e–g except computed based on the scheme of Klepp et al. (2003).



bration of Tucker’s scheme using a more extensivedataset should be conducted.

The results shown in Figs. 5 and 8d–f are based onour modification to Tucker’s scheme. However, theseresults are not very sensitive to the exact scheme used.In Figs. A3a–c, we show the results corresponding toFigs. 5e–g, but computed using the scheme of DB78,while in Figs. A3d–f similar results are computed withthe scheme by Klepp et al. (2003). It is clear that theseasonal cycle seen in Figs. 5e–g is still observable inFig. A3. For the results shown in Figs. A3d–f, we have

confirmed that nearly all of the seasonal differences arisefrom the categories that are quantified by Klepp et al.

APPENDIX B

Composites Based on 1958–81 Data

The results shown in the main text are computedbased on data from 1985 to 2002. Similar compositeshave been made based on data from 1958 to 1981 (ex-cept for satellite-retrieved precipitation, which is not

FIG. B1. (a)–(c) As in Figs. 4e–g except computed based on data from 1958–1981; (d)–(f)as in Figs. 5e–g except computed based on data from 1958 to 1981.



available for that period). The difference in the distri-bution of precipitation between winter and the otherseasons for western North Pacific cyclones, computedbased on VSAMP ERA-40 data, is shown in Figs. B1a–c, while those computed based on VOS-observed pre-cipitation frequency are shown in Figs. B1d–f (theseshould be compared to Figs. 4e–g and 5e–g, respec-tively). Clearly, similar patterns can be observed duringthe two periods for both ERA-40 data and VOS obser-vations. Other results are also similar and are notshown.

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The Seasonal Cycles in the Distribution of Precipitation