Determining Key Predictors for NCAR’s Convective Auto-Nowcast System Using Climatological Analyses Thomas Saxen, Cindy Mueller, and Nancy Rehak National

Determining Key Predictors for NCAR’s Convective Auto-Nowcast System Using

Climatological Analyses

Thomas Saxen, Cindy Mueller, and Nancy RehakNational Center for Atmospheric Research,

Boulder, Colorado 80307

Corresponding author address: Thomas Saxen, NCAR/RAL, Boulder, CO 80307; email: [email protected]

Overview of Paper

• Overview of how Auto-Nowcast (ANC) system works

• Description of how the forecast logic is currently setup

• Climatological analyses of storm initiation locations

• Future research ideas

• Summary

Overview of How ANC System Works

• The NCAR ANC system (described in Mueller et al., 2003) is an expert system that utilizes fuzzy logic (McNeill and Freiberger 1993) to produce short term (0-1 hr) nowcasts of storm initiation, growth and decay. The ability of the ANC system to forecast storm initiation is what makes the system unique. The focus of this paper will be on the initiation component of the forecast logic.

• The ANC system ingests various data sets (including single or multiple WSR-88D radar, satellite, surface, sounding, and numerical model data). Subsequent algorithms utilize these data sets to produce predictor fields that are transformed into interest fields by applying membership functions to the various predictor fields. An example of this is given on the following slides where a description of how the information is combined in the ANC system is given.

• The system also allows human users to enter boundary locations (which are tracked automatically in real-time) and polygons (which can be used to adjust the initiation interest field).

How the ANC System Works?

875 - 725 mb Mean Relative Humidity

-1

-0.5

0

0.5

1

0 20 40 60 80 100

%

Inte

restAn input predictor field

Example: Avg RH Field

Example Membership Fn

Resulting Interest FieldExample: Avg RH Interest Field

The resulting interest fields all have values between -1 and 1.

How the ANC System Works?

• The resulting interest fields are weighted (using their associated weighting factor) and then summed to give the initiation interest field.

• A threshold is applied to the initiation interest field and then it is combined with the growth and decay part of the forecast to give the final forecast field.

• In the figure to the right, the grey-shades are the initiation component of the forecast and the warm-shades are the growth and decay component. The yellow lines are the human-entered boundaries and the magenta lines are the 60 minute extrapolated location.

List of Predictor Fields Currently Being Used in the ANC System

• Environmental conditions (RUC)– Frontal likelihood– Layered stability– CAPE (max between 900 and 700 mb)– Mean 875 to 725 mb Relative Humidity

• Boundary-layer– Convergence– LI (based on METARS)– Vertical velocity along boundary (maxW)– Boundary-relative steering flow– New storm development along boundary

• Clouds– Clear or Cumulus– Vertical develop as observed by drop in

IR temps

• In this paper, we will focus on environmental conditions information from the Rapid Update Cycle (RUC) model (Benjamin, et al., 2004).

• The RUC fields listed here will be explored along with some additional RUC-based predictor fields that are not currently being used in the ANC forecast logic.

Description of How the Forecast Logic is Currently Setup

• Currently the forecast logic is setup using physically based reasoning based on previous research results to define the various membership functions and their associated weighting factors.

• Once the initial set of rules are devised, the logic is tested on several (usually 4-6) representative cases from the area where the system is being deployed.

• Based on these tests, the membership functions and weights are adjusted to attain optimal results for the chosen cases.

• Depending on the cases chosen, the forecast logic sometimes needs further adjustments after running operationally.

Climatological Analyses of Storm Initiation Locations

• An improved method for determining the key input predictor fields and the weights that they are given based on climatological analyses will be described in the following pages.

• Predictor field data for areas where storms initiate along with areas where storms do not initiate are analyzed. Cumulative probability distributions of predictor field values for both areas are presented along with ideas on how these can be used to better define the membership functions and the associated weights that they are given in the forecast logic.

• This method utilizes much more data than the current method and therefore should result in a more robust set of forecast logic.

Climatological Analyses of Storm Initiation Locations (Method Description)

• TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting; Dixon and Weiner, 1993) software was used to identify storm initiation locations in the ANC domain from 25 May 2004 through 31 July 2004.

• The initiation locations were mapped to a Cartesian grid and expanded spatially by 40 km.

• The gridded initiation location data was then matched to numerous model (RUC) based predictor fields that would have been available at the forecast generation time.

• Histograms of the various predictor fields were then produced for the storm initiation and no initiation areas.

• The histograms were converted to cumulative probability distributions and will be discussed on the following pages.

Climatological Analyses of Storm Initiation Locations (Results)

• The RUC 875-725 mb mean RH plot shows that higher RH values are more conducive to storm initiations.

• This suggests that the membership function should be negative below about 50% RH and be given a large positive values above about 65% RH.

• The differences between the two distributions suggest that this predictor field should be given a relatively high weight.

• This is very similar to the way this field is currently used in the forecast logic.

Proposed Membership Function


• The frontal likelihood field is a field which tries to identify frontal areas based on vorticity, convergence, and gradients in Theta E in the low level RUC data.

• The two distributions look quite similar suggesting that this field may not be overly useful in identifying initiation regions and if used should not be given a large weight.

• In the proposed membership function we focus on the extreme ends of the distribution.

• It is thought that this field may show better results if the data was separated into different types of convective organization (i.e. scenarios). This will be explored in the future.



• The maximum CAPE (calculated from RUC data) between 900 and 700 mb plot shows that higher CAPE values are more conducive to storm initiations.

• This suggests that the membership function should be negative for very low CAPE values and then turn positive for CAPE values greater than about 100 J/kg.

• The differences between the two distributions suggest that this predictor field should be given a relatively high weight.

• This suggests that we need to adjust our current membership function so that it is more negative at CAPE values near zero and then jumps to more neutral values at small values and then increases with higher values.



• The maximum CIN values (calculated from RUC data) between 975 and 900 mb histograms do not show much difference between the storm initiation and no storm initiation areas.

• This suggests that this field should not be given much weight.

• This field is used to help suppress initiation during periods when there is a very strong cap. Therefore, the proposed membership function only contains negative interest values at higher values of CIN.

• This analysis suggests that the use of this field should be explored further to verify it’s usefulness in the forecast logic.



• The vertical sum interest predictor field is a field which counts the number of unstable layers in the RUC column. It shows that there is increased likelihood for initiation when there are multiple unstable layers.

• This suggests that the membership function should be weakly negative for very small values and increase as more unstable layers are deduced in the RUC data.

• Since you do get a significant number of initiations with small or no unstable layers in the RUC data, the weight given to this predictor field will be small.

• This field is suspected to give better results for nocturnal convection and may also be dependant on the type of convective organization. These will need further investigation.



• The vertical shear field provides an estimate of the magnitude of shear in the RUC data between 975 and 725 mb.

• At low values, the distributions look very similar, but at higher values there is an enhanced tendency to get more convective initiation.

• This field is not currently used in the forecast logic. The proposed membership function has weak negative values for low shear with increasing interest as the shear magnitude strengthens.

• Since the distributions for both the storm initiation and no initiation areas have similar shapes, the weight with which this field will be used will be small.



• The RUC 1000 mb Theta E distributions show that storm initiations are more likely when the Theta E values are higher.

• This predictor field is not currently used in the forecast logic, but this analysis suggests that this would be a good addition.

• Since the distributions show a nice separation this predictor field should be given a fairly large weight in the forecast logic.



• The maximum RUC Theta E advection between 1000 and 900 mb distributions show a slight enhancement in the initiation likelihood with positive values of Theta E advection.

• This predictor field is not currently used in the forecast logic. Since the distributions are very similar it suggests that if this field is used it would be given a small weight and the membership function would focus primarily on the positive values of Theta E advection.


Future Research

• Other predictor fields (i.e. satellite and surface based observations) will be analyzed in a similar fashion.

• Current membership functions and weights will be modified and potential new predictor fields will be added to the forecast logic based on these results.

• The effect that different convective scenarios (i.e. squall lines, supercells, isolated convective cells, etc.) have on the resulting distributions will be explored. It is suspected that this analysis will help isolate which fields are the most beneficial for the different types of expected convective organization.

Summary• A method for using climatological analyses to help define membership functions and

weights for the NCAR ANC system has been presented.• This method has advantages over the currently used method in that much more data

is used and therefore, the dependence on the choices for the tuning cases is minimized.

• The differences between the histograms for the storm initiation and no storm initiation areas can be used to better define the membership function and also give an indication of the weight that should be given to the respective fields in the forecast logic.

• The results show that some fields currently being used are good choices, but their membership functions may be better defined and their weights in the forecast logic may need to be modified (i.e. Average RH, CAPE, and Vertical Sum Interest).

• The results also show that some fields that are currently not being used may be good choices for inclusion in the forecast logic, especially the 1000 mb Theta E field.

• Similar analyses of the other fields (both currently being used and not being used) needs to be completed.

• The results of these analyses need to be tested on independent data.• Further research is required to determine if separating the data into different types of

convective organization (or scenarios) would lead to some fields that do not show markedly different distribution having more useful characteristics.

References

Benjamin, S. G., D. Devenyi, S. S. Sygandt, K. J. Brundage, J. M. Brown, G. A. Grell, D. Kim, B. E. Schwartz, T. G. Smirnova, T. L. Smith, and G. S. Manikin, 2004: An hourly assimilation-forecast cycle: The RUC. Mon. Wea. Rev., 132. 495-518.

Dixon, M., and G. Weiner, 1993: Thunderstorm identification, tracking, analysis, and nowcasting – a radar-based methodology. J. Atmos. Oceanic Tech., 10, 785-797.

McNeill, D. and P. Freiberger, 1993: Fuzzy Logic, The Revolutionary Computer Technology that is Changing Our World. Simon & Schuster, Pg 319.

Mueller, C., T. Saxen, R. Roberts, J. Wilson, T. Betancourt, S. Dettling, N. Oien, and J. Yee, 2003: NCAR Auto-Nowcast system. Wea. Forecasting, 18, 545-561.

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Determining Key Predictors for NCAR’s Convective Auto-Nowcast System Using Climatological Analyses Thomas Saxen, Cindy Mueller, and Nancy Rehak National