Progress on the new crimss ocean land precipitation detection algorithm
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Progress on the New CrIMSS Ocean/Land Precipitation Detection Algorithm. Flavio Iturbide-Sanchez 1 , Murty Divarkala 1 , Mike Wilson 1 Changyi Tan 1 , Xiaozhen Xiong 1 and Tony Reale 2 1 IMSG at NOAA/NESDIS/STAR 2 NOAA/NESDIS/STAR Wenze Yang 3 and Ralph Ferraro 2

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Progress on the new crimss ocean land precipitation detection algorithm

Progress on the New CrIMSS Ocean/Land Precipitation Detection Algorithm

Flavio Iturbide-Sanchez1, Murty Divarkala1, Mike Wilson1Changyi Tan1, Xiaozhen Xiong1 and Tony Reale2

1IMSG at NOAA/NESDIS/STAR

2NOAA/NESDIS/STAR

Wenze Yang3 and Ralph Ferraro2

3NOAA Corporate Institute for Climate and Satellites


Outline

  • Implementation of the Algorithm.

  • Description of the Algorithm.

  • Qualitative Comparisons.

  • Quantitative Analysis.

  • Summary.


Subroutines Implemented and Programs Modified as part of the New Precipitation Detection Algorithm based on the MSPPS Rainfall Rate Algorithm

MSPPS: Microwave Surface and Precipitation Products


The New New Precipitation Detection Algorithm based on the MSPPS Rainfall Rate AlgorithmCrIMSS Precipitation Detection Algorithm for Ocean/Land Surfaces

Convergence in the MW-only Retrieval

Observation: The new precipitation detection algorithm relies in the quality of the microwave retrieved skin temperature . However, in cases were the MW-only retrieval does not converge, the precipitation detection algorithm is applied too. Those cases will be analyzed carefully since the Tskin values used could lead to false alarms or under detection of precipitation.

Input Variables Needed:

  • Microwave Brightness Temperatures (K).

  • Microwave retrieved Skin Temperature (K) .

  • Local Zenith Angle (deg).

  • Average land fraction in FOR (for surface type definition).

  • Latitude and Longitude (deg).

    Assumptions Made:

  • Fix Wind Speed=10 m/s.

  • Precipitation flag is determined even in cases were the MW retrieval does not converge.

Yes

No

Detect Precipitation over ocean or land (TB, LZA, SfcType, Lat, Lon, Tskin (MW-only))


Justification about the use of a Wind Speed=10 m/s New Precipitation Detection Algorithm based on the MSPPS Rainfall Rate Algorithm

Tsknfrom MIRS N18, WindSpeed=10m/s, CLW, TPW and IWP from MSPPS N18

MSPPS N18 Rainfall Rate


Current New Precipitation Detection Algorithm based on the MSPPS Rainfall Rate AlgorithmCrIMSS MX7.0 Precipitation Flag

CrIMSS NPP Precipitation Flag Descending

CrIMSS NPP Precipitation Flag Ascending

False alarms over higher latitudes

Current Precipitation Detection Algorithm

Low Probability of detection over the tropics

Under-detection cases

False Alarms


CrIMSS New Precipitation Detection Algorithm based on the MSPPS Rainfall Rate Algorithm MX7.0 + New Precipitation Flag Algorithm

CrIMSS NPP Precipitation Flag Descending

CrIMSS NPP Precipitation Flag Ascending

Observations:

  • Reduction in the number of false alarms in the higher latitudes.

  • Improvements in the detection over the tropic regions.

Current Precipitation Detection Algorithm

Under-detection cases

False Alarms


MSPPS Rainfall Rate New Precipitation Detection Algorithm based on the MSPPS Rainfall Rate Algorithm

MSPPS NPP/ATMS Descending

MSPPS NPP/ATMS Ascending

Differences observed in the fields of precipitation of CrIMSS and MSPPS are related to: 1) the fixed wind speed (10 m/s) and 2) the quality of skin temperature used in the CrIMSS precipitation detection algorithm, and to 3) the frequency and polarization differences found between the Channel 3 and 7 of the ATMS and AMSUA/MHS sensors (CrIMSS new precipitation flag is based on an MSPPS precipitation algorithm optimized for an AMSUA/MHS sensor architecture, while the MSPPS NPP/ATMS Precipitation algorithm is already optimized for the NPP/ATMS sensor).

NPP/ATMS: Ch3 (50.3 GHz, H), Ch17(165.5, H) AMSUA/MHS: Ch3 (50.3 GHz, V), Ch17(157, V)


Categorical Scores for the Evaluation of Precipitation: Probability of Detection (POD) and False Alarm Rate (FAR)

Observed

z

y

x

Estimated

w


Evaluation of the Probability of Detection (POD) and False Alarm Rate (FAR)CrIMSS Ocean/Land Precipitation Detection Algorithm using the MSPPS Rainfall Rate

Increase in the POD and reduction in the FAR

Performance of New Precipitation Detection Algorithm

Similar number of precipitation cases for RR > 0.25 mm/h

Precipitation cases found in MSPPS are consistently higher than CrIMSS precipitation cases

The FAR is consistently larger than the POD

Performance of Current Precipitation Detection Algorithm


Summary Probability of Detection (POD) and False Alarm Rate (FAR)

  • A new ocean/land precipitation algorithm has been embedded as part of the CrIMSS offline algorithm version MX7.0.

  • Results show improvement in the detection of precipitation (particularly over tropic regions), while having small number of false alarms over high latitudes.

  • The detection capability of the new precipitation detection algorithm behaves comparable to the MSPPS precipitation algorithm for precipitations above 0.25 mm/h.

  • The impact of the new precipitation detection algorithm in the performance of the retrieved CrIMSS EDRs is in progress.


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