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Contrasting Tropical Rainfall Regimes Using TRMM and Ground-Based Polarimetric Radar by S. A. Rutledge, R. Cifelli, T. Lang and S. W. Nesbitt EGU 2009.

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Presentation on theme: "Contrasting Tropical Rainfall Regimes Using TRMM and Ground-Based Polarimetric Radar by S. A. Rutledge, R. Cifelli, T. Lang and S. W. Nesbitt EGU 2009."— Presentation transcript:

1 Contrasting Tropical Rainfall Regimes Using TRMM and Ground-Based Polarimetric Radar by S. A. Rutledge, R. Cifelli, T. Lang and S. W. Nesbitt EGU 2009 Session AS1.3

2 The TRMM satellite has provided unprecedented data for over 10 years. TRMM precipitation products have advanced our understanding of tropical precipitation considerably. We used ground-based polarimetric radar data from two field campaigns, TRMM-LBA (1999) and NAME (North American Monsoon Experiment; 2004), to characterize the physical nature of precipitation in these regions (tropical Brazil and Mexico). We focus on comparing rain rate and reflectivity distributions between the TRMM PR and the ground based radar data. TRMM LBA NAME

3 East-West Regimes found in TRMM-LBA. East regime associated with high CAPE, vigorous convection and frequent lightning. West regime associated with lower CAPE, weaker convection and lower flash rates. January-February 1999.

4 Strong topographical influence on precipitation intensity and structure. For both NAME and LBA, observations suggest equilibrium DSD indicated by near constant D 0 values at high rain rates/reflectivities.

5 Mean TRMM reflectivity values are lower compared to S-pol below 7-8 km. Above this level TRMM PR is higher mainly due to 17 dB cut off for the PR. Are the reflectivity differences below 7-8 km a result of attenuation correction issues for the PR (wet ice, Mie effects), sampling issues (many more PR profiles compared to ground based profiles), or other factors? S-pol resolution has been degraded

6 For NAME, PR reflectivities are again less than those for S-pol in a vertical profile sense. Near surface values are in better agreement with S-pol. In a mean profile sense, and statistically, the PR reflectivities are lower compared to ground based observations for both locales. PR profile remains below S-pol profile. Stronger convection in NAME.

7 TRMM PR does not see the high rain rates observed in the S-pol spectrum TRMM PR places more rain volume at lower rain rates compared to the ground based radar. Turn now to examination of rain rates between PR and S-pol for TRMM-LBA

8 NAME shows similar behavior, especially over water and the coastal plains. At higher elevations, TRMM PR and S-pol rain rate spectrum is in better agreement. This may be due to reduced S-pol resolution (compared to PR) at long ranges required to sample storms over the higher terrain. Over waterCoastal plain 500-1500 m> 1500 m And the same analysis for NAME

9 For NAME, TRMM PR places more rain volume at lower rain rates compared to the ground based radar. Consistent with finding for TRMM-LBA. 10-15% differences in rain volume seen for this particular comparison.

10 Z-R plot for LBA Solid--S-pol Dash--PR Above 10 mm/h, PR reflectivity corresponds to lower rain rate compared to same reflectivity for S-pol.

11 Over water Over coastal plains Z-R relationships in fair agreement over water. Z-R relationships in lesser agreement over adjacent coastal plains and higher topographical elevations (not shown). NAME For a given Z, TRMM Z-R produces less rain compared to S–pol for R > 10 mm/h

12 Summary To the extent that the differences in the PR and ground based data records don’t matter….. TRMM PR reflectivities reduced over land compared to ground based observations in two field projects. Does this reflect issues with attenuation correction over land? TRMM generated rain rates are lower than ground based radar estimates for a given reflectivity. Single polarization, radar-based rain estimates such as those from the PR are not capable of capturing the full physics of the DSD. So even if the Z’s were to match…. Evidence for equilibrium DSD being achieved in these tropical rain regimes; drop break up produces smaller drops. This process complicates Z based rain estimation. Appeal next to Darwin dataset that is much longer than field campaign data Acknowledge long term support from NASA TRMM and PMM programs

13 DSD differences can be seen in distribution of A coefficient in Z = AR b DSD Attenuation correction Rain estimation


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