Current Operational Data Capabilities, Current Operational Data Capabilities, Issues and PerspectivesIssues and Perspectives

Chandra KondraguntaChandra Kondragunta

Hydrometeorology GroupHydrometeorology GroupHydrology LaboratoryHydrology Laboratory

Office of Hydrologic DevelopmentOffice of Hydrologic DevelopmentNOAA/National Weather ServiceNOAA/National Weather Service

Q2 WorkshopQ2 WorkshopNorman, OKNorman, OK

June 28, 2005 June 28, 2005

OutlineOutline

. QPE requirements for NWS operations . QPE requirements for NWS operations

. Current operational data capabilities and issues. Current operational data capabilities and issues

. Potential other data sets for QPE. Potential other data sets for QPE

. Field perspectives. Field perspectives

. Summary. Summary

QPE Requirements QPE Requirements

Priority Category : “1” = Mission Critical : Cannot meet Priority Category : “1” = Mission Critical : Cannot meet operational mission objectives without this data setoperational mission objectives without this data set

Threshold ObjectiveThreshold Objective

Spatial Res. 1 km 0.5kmSpatial Res. 1 km 0.5km

Temporal Res. 6 min. 1 min.Temporal Res. 6 min. 1 min.

Accuracy 1 mm/hour 0.25 mm/hourAccuracy 1 mm/hour 0.25 mm/hour

Data Latency 3 min. 1 min.Data Latency 3 min. 1 min.

Mapping Accuracy 0.2 km 0.1 kmMapping Accuracy 0.2 km 0.1 km

Current operational data capabilities Current operational data capabilities and issuesand issues

Data SourcesData Sources

Current sources of data for QPE in NWS:Current sources of data for QPE in NWS:

1. Rain gauge data1. Rain gauge data

2. WSR-88D radar rainfall estimates2. WSR-88D radar rainfall estimates

3. Satellite Precipitation Estimates3. Satellite Precipitation Estimates

4. NWP model output4. NWP model output

Rain Gauge Data Rain Gauge Data

Rain gauge data for NWS operations come from several different sources:Rain gauge data for NWS operations come from several different sources:

HADS:HADS:. Federal & State Wildland Fire Programs --- 2,400 rain gages. Federal & State Wildland Fire Programs --- 2,400 rain gages. USGS --- 1,734 rain gages. USGS --- 1,734 rain gages. USACE --- 1,637 rain gages. USACE --- 1,637 rain gages. NWS --- 222 rain gages. NWS --- 222 rain gages. 117 other DCS Platform operators (USBR, TVA etc.). 117 other DCS Platform operators (USBR, TVA etc.)

Other:Other:

. S. State and local government funded agencies (Mesonets)tate and local government funded agencies (Mesonets)

. Automated Surface Observing System. Automated Surface Observing System

. Cooperative rain gauge network. Cooperative rain gauge network

. Other NWS supported gauges (IFLOWS, ALERT etc.). Other NWS supported gauges (IFLOWS, ALERT etc.)

Spatial resolution : Non-uniform Spatial resolution : Non-uniform Temporal resolution : hourly and daily (few 1 min. gauges)Temporal resolution : hourly and daily (few 1 min. gauges)

HADSHADS

Hydrometeorological Automated Data System:Hydrometeorological Automated Data System:

. An integrator of in situ data. An integrator of in situ data

. Acquires non-standard raw data relayed via GOES Data . Acquires non-standard raw data relayed via GOES Data Collection System (DCS)Collection System (DCS)

. More than 1.7 million observational values processed . More than 1.7 million observational values processed each dayeach day

. 11,500 data reporting locations. 11,500 data reporting locations

. 97% of data network is non-NOAA . 97% of data network is non-NOAA

. A future component of NOAA’s Integrated Surface . A future component of NOAA’s Integrated Surface Observing System (ISOS) ProgramObserving System (ISOS) Program

Issues with rain gauge dataIssues with rain gauge dataThere are several issues with rain gauge data: There are several issues with rain gauge data:

Coverage : Uneven spatial and temporal coverage, Sparse network Coverage : Uneven spatial and temporal coverage, Sparse network density for some regions density for some regions

Quality : Gauge data quality is a big problemQuality : Gauge data quality is a big problemExamples: Transmission errors, staggered reporting times, frozen Examples: Transmission errors, staggered reporting times, frozen

gauges, outliers, missing data etcgauges, outliers, missing data etc

Timeliness : Reports arriving lateTimeliness : Reports arriving late Errors: Errors: . Wind effects --- Under catch. Wind effects --- Under catch . Gauge exposure blockages (trees, buildings etc.) --- Under catch. Gauge exposure blockages (trees, buildings etc.) --- Under catch . Solid precipitation --- under catch. Solid precipitation --- under catch . Heavy rain rates --- under catch. Heavy rain rates --- under catch . Strong wind --- over catch. Strong wind --- over catch

Density is uneven and poor in Nevada

Radar Rainfall EstimatesRadar Rainfall Estimates

Current radar rainfall estimates come from Current radar rainfall estimates come from WSR88D radar networkWSR88D radar network

Spatial resolution : 2km x 1 Deg.Spatial resolution : 2km x 1 Deg.

Temporal resolution : 6 min.Temporal resolution : 6 min.

Issues: Beam blockage, under estimation, over Issues: Beam blockage, under estimation, over estimation, detection problem, Anomalous estimation, detection problem, Anomalous propagation etc.propagation etc.

Effective CNRFC Radar Coverage Effective NWRFC Radar Coverage

Satellite Precipitation EstimatesSatellite Precipitation Estimates

Current Satellite Precipitation Estimates (SPE) Current Satellite Precipitation Estimates (SPE) come from GOES satellite. They are generated come from GOES satellite. They are generated by an algorithm called the HydroEstimator.by an algorithm called the HydroEstimator.

Spatial resolution : 4 kmSpatial resolution : 4 km

Temporal resolution : 15 min.Temporal resolution : 15 min.

Issues: Under estimation, over estimation, Issues: Under estimation, over estimation, detection problem, mis-location of precipitationdetection problem, mis-location of precipitation

NWP outputNWP output

Several NWP model outputs such as RUC, MM5, Several NWP model outputs such as RUC, MM5, MOS, NDFD etc. are used in operationsMOS, NDFD etc. are used in operations

Spatial resolution : 5 kmSpatial resolution : 5 km

Temporal resolution : 1 hrTemporal resolution : 1 hr

Issue: Accuracy of model output Issue: Accuracy of model output

MPEMPEMulti-sensor Precipitation Estimator (MPE) is an operational Multi-sensor Precipitation Estimator (MPE) is an operational

software currently being used at several NWS field offices to software currently being used at several NWS field offices to generate QPE.generate QPE.

It uses rain gauge, radar and satellite precipitation estimates to It uses rain gauge, radar and satellite precipitation estimates to generate multi-sensor QPEgenerate multi-sensor QPE

The main features of MPE are:The main features of MPE are:. Delineation of effective radar coverage . Delineation of effective radar coverage . Mosaicking based on radar sampling geometry. Mosaicking based on radar sampling geometry. Service area-wide precipitation analysis. Service area-wide precipitation analysis. Mean field bias correction of radar rainfall estimates. Mean field bias correction of radar rainfall estimates. Local bias correction of radar and satellite precipitation . Local bias correction of radar and satellite precipitation

estimatesestimates. Semi-automated rain gauge QC tools. Semi-automated rain gauge QC tools. Several GUI tools to interactively modify the point values or . Several GUI tools to interactively modify the point values or

gridded fieldsgridded fields

ORPG/PPS

RFC

Multi-Sensor PrecipitationEstimator (MPE)

WSR-88DDPA

Hydro-Estimator

Rain Gauges

Mean Field/local Bias correction

MPE

Local Bias correction

MEAN FIELD BIAS (MFB) ADJUSTMENT

Before Correction After Correction

MULTISENSOR (GAUGE+RADAR) ESTIMATION FILLS MISSING AREAS

Bias Corrected Multi-sensor

Hydroestimator (mm) Local Bias-Corrected Hydroestimator

CNRFC 24-Hour Precipitation, 17 Dec 2002

Gauge QC in MPEGauge QC in MPE

Spatial Consistency Check (semi-automated): Spatial Consistency Check (semi-automated): . Checks for consistency of a gauge value with the . Checks for consistency of a gauge value with the

neighboring gauge values in all four quadrantsneighboring gauge values in all four quadrants . Lightning data is used to screen the gauges received . Lightning data is used to screen the gauges received

rainfall from convective systems before flagging the outliersrainfall from convective systems before flagging the outliers

Multi-Sensor Check (semi-automated): Multi-Sensor Check (semi-automated): . Compares the rain gauge values with radar estimates and . Compares the rain gauge values with radar estimates and

points out the stuck gaugespoints out the stuck gauges

Display 7X7:Display 7X7: . Ability to display 7X7 HRAP bins centered on a gauge to . Ability to display 7X7 HRAP bins centered on a gauge to

aid manual gauge QCaid manual gauge QC

Spatial Consistency Check

Multi-sensor check

Locally Grown CapabilitiesLocally Grown Capabilities

Some of the locally grown software areSome of the locally grown software are

. Mountain Mapper : To generate gridded QPE, . Mountain Mapper : To generate gridded QPE, gauge QC (mostly in the western region)gauge QC (mostly in the western region)

. XNAV, XDAT to QC gauge data. XNAV, XDAT to QC gauge data

Potential other data sets for QPEPotential other data sets for QPE

. Reflectivity data from the Terminal Doppler . Reflectivity data from the Terminal Doppler Weather RadarWeather Radar

. Canadian radar data (NMQ). Canadian radar data (NMQ)

. Microwave satellite precipitation estimates from . Microwave satellite precipitation estimates from SSM/I sensorsSSM/I sensors

. Precipitation estimates from the TRMM . Precipitation estimates from the TRMM

. Lightning data. Lightning data

Field PerspectivesField Perspectives

Rain Gauge DataRain Gauge Data

. . “There are always issues with rain gauge data. Missing data, “There are always issues with rain gauge data. Missing data, Zero reports, transmission errors, tipping bucket errors, poorly Zero reports, transmission errors, tipping bucket errors, poorly maintained equipment (particularly with IFLOWS) staggered maintained equipment (particularly with IFLOWS) staggered reporting times etc.” --- OHRFCreporting times etc.” --- OHRFC

(Several other RFCs expressed similar view point)(Several other RFCs expressed similar view point)

. . “High elevation data, such as SNOTEL has problems because “High elevation data, such as SNOTEL has problems because of the freezing of the gauge” --- NWRFC of the freezing of the gauge” --- NWRFC

. “. “WGRFC has numerous gauge – sparse areas over roughly the WGRFC has numerous gauge – sparse areas over roughly the western half of our region. Gauges are densely clustered in our western half of our region. Gauges are densely clustered in our largest cities due to ALERT systems. There can be issues with largest cities due to ALERT systems. There can be issues with data quality and timeliness within these systems” --- WGRFCdata quality and timeliness within these systems” --- WGRFC

Radar DataRadar Data . “Over and under estimation, significant gaps in coverage, . “Over and under estimation, significant gaps in coverage,

lack of coverage of basins in Canada, gross lack of coverage of basins in Canada, gross underestimation in winter, inconsistent Z/R relationships in underestimation in winter, inconsistent Z/R relationships in adjoining radars” --- NCRFCadjoining radars” --- NCRFC

(Several RFC expressed similar view point)(Several RFC expressed similar view point)

. “ Radar data in our area is of no use in generating QPE. . “ Radar data in our area is of no use in generating QPE. Beam blockage, inadequate coverage, melting level bright Beam blockage, inadequate coverage, melting level bright band etc.” --- CNRFCband etc.” --- CNRFC

. “Radar is useless in the NWRFC area” --- NWRFC . “Radar is useless in the NWRFC area” --- NWRFC

. “We use MPE and have the usual radar issue: under/over . “We use MPE and have the usual radar issue: under/over estimation of rainfall, radar coverage, bright banding …” estimation of rainfall, radar coverage, bright banding …” LMRFCLMRFC

Satellite Precipitation EstimatesSatellite Precipitation Estimates

. . “We don’t use satellite precipitation estimates because of poor “We don’t use satellite precipitation estimates because of poor quality” --- LMRFCquality” --- LMRFC

(Most of the RFCs expressed similar view point)(Most of the RFCs expressed similar view point)

. “ We use it EXTREMELY rarely, when there is no other data. . “ We use it EXTREMELY rarely, when there is no other data. Maybe 1 time in 10000 cases. It has proven to be of poor Maybe 1 time in 10000 cases. It has proven to be of poor quality for the most part ” --- ABRFCquality for the most part ” --- ABRFC

. . “WGRFC supplements the radar void regions with SPE.” --- “WGRFC supplements the radar void regions with SPE.” --- WGRFCWGRFC

SummarySummary

In summary,In summary,

. Rain gauge data quality is an issue for current NWS hydrologic . Rain gauge data quality is an issue for current NWS hydrologic operations. Need to develop automated gauge QC techniques operations. Need to develop automated gauge QC techniques to satisfy the next generation QPE algorithm demandsto satisfy the next generation QPE algorithm demands

. Need to improve the rain gauge network density to improve the . Need to improve the rain gauge network density to improve the data coveragedata coverage

. Need to address the radar coverage gap issues by bringing in . Need to address the radar coverage gap issues by bringing in alternate data sets, such as satellite precipitation estimates and alternate data sets, such as satellite precipitation estimates and NWP model outputNWP model output

. Need to address the radar rainfall estimation issues (over/under . Need to address the radar rainfall estimation issues (over/under estimation) estimation)

Summary (Contd.)Summary (Contd.)

. Need to improve the satellite precipitation quality by developing . Need to improve the satellite precipitation quality by developing

multi-platform, multi-sensor (IR+MW) techniquesmulti-platform, multi-sensor (IR+MW) techniques

. Need to make better use of NWP model output in QPE . Need to make better use of NWP model output in QPE estimationestimation

Questions?Questions?

COOP network

Rain gauge in snow

Rain gauge site