scholarly journals Raindrop Size Distribution Modeling from a Statistical Rain Parameter Relation and Its Application to the TRMM Precipitation Radar Rain Retrieval Algorithm

2009 ◽  
Vol 48 (4) ◽  
pp. 716-724 ◽  
Author(s):  
Toshiaki Kozu ◽  
Toshio Iguchi ◽  
Toyoshi Shimomai ◽  
Nobuhisa Kashiwagi
Keyword(s):  
Size Distribution ◽  
Tropical Rainfall Measuring Mission ◽  
Scale Model ◽  
Retrieval Algorithm ◽  
Precipitation Radar ◽  
Temporal Domain ◽  
Raindrop Size Distribution ◽  
Trmm Precipitation ◽  
Raindrop Size ◽  
Parameter Relation

Abstract A generalized method is presented to derive a “two scale” raindrop size distribution (DSD) model over a spatial or temporal domain in which a statistical rain parameter relation exists. The two-scale model is generally defined as a model in which one DSD parameter is allowed to vary rapidly and the other is constant over a certain space or time domain. The existence of a rain parameter relation such as the radar reflectivity–rainfall rate (Z–R) relation over a spatial or temporal domain is an example of such a two-scale DSD model. A procedure is described that employs a statistical rain parameter relation with an assumption of the gamma DSD model. An example using Z–R relations obtained at Kototabang, West Sumatra, is presented. The result shows that the resulting two-scale DSD model expressed by conventional DSD parameters depends on the assumed value of parameter μ while rain parameter relations such as k–Ze relations from those models using different μ values are very close to each other, indicating the stability of the model against the variation of μ and the validity of this method. The result is applied to the DSD model for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar 2A25 (versions 5 and 6) algorithm. The derivation procedure of the 2A25 DSD model is described. Through the application of this model, it has become possible to make a logically well-organized rain profiling algorithm and reasonable rain attenuation correction and rainfall estimates, as described in an earlier paper by Iguchi et al.

scholarly journals Relationships between the Raindrop Size Distribution and Properties of the Environment and Clouds Inferred from TRMM

2012 ◽  
Vol 25 (8) ◽  
pp. 2963-2978 ◽  
Author(s):  
S. Joseph Munchak ◽  
Christian D. Kummerow ◽  
Gregory Elsaesser
Keyword(s):  
Size Distribution ◽  
Large Scale ◽  
Deep Convection ◽  
Principal Component ◽  
Tropical Rainfall Measuring Mission ◽  
Mean Values ◽  
Tropical Regions ◽  
Tropical Environments ◽  
Raindrop Size Distribution ◽  
Raindrop Size

Abstract Raindrop size distribution (DSD) retrievals from two years of data gathered by the Tropical Rainfall Measuring Mission (TRMM) satellite and processed with a combined radar–radiometer algorithm over the oceans equatorward of 35° are examined for relationships with variables describing properties of the vertical precipitation profile, mesoscale organization, and background environment. In general, higher freezing levels and relative humidities (tropical environments) are associated with smaller reflectivity-normalized median drop size (εDSD) than in the extratropics. Within the tropics, the smallest εDSD values are found in large, shallow convective systems where warm rain formation processes are thought to be predominant, whereas larger sizes are found in the stratiform regions of organized deep convection. In the extratropics, the largest εDSD values are found in the scattered convection that occurs when cold, dry continental air moves over the much warmer ocean after the passage of a cold front. These relationships are formally attributed to variables describing the large-scale environment, mesoscale organization, and profile characteristics via principal component (PC) analysis. The leading three PCs account for 23% of the variance in εDSD at the individual profile level and 45% of the variance in 1°-gridded mean values. The geographical distribution of εDSD is consistent with many of the observed regional reflectivity–rainfall (Z–R) relationships found in the literature as well as discrepancies between the TRMM radar-only and radiometer-only precipitation products. In particular, midlatitude and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Ocean intertropical convergence zone.

An Observationally Generated A Priori Database for Microwave Rainfall Retrievals

2011 ◽  
Vol 28 (2) ◽  
pp. 113-130 ◽  
Author(s):  
Christian D. Kummerow ◽  
Sarah Ringerud ◽  
Jody Crook ◽  
David Randel ◽  
Wesley Berg
Keyword(s):  
A Priori ◽  
Tropical Rainfall Measuring Mission ◽  
Optimal Estimation ◽  
Precipitable Water ◽  
Estimation Procedure ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Precipitation Radar ◽  
Brightness Temperatures ◽  
Trmm Precipitation

Abstract The combination of active and passive microwave sensors on board the Tropical Rainfall Measuring Mission (TRMM) satellite have been used to construct observationally constrained databases of precipitation profiles for use in passive microwave rainfall retrieval algorithms over oceans. The method uses a very conservative approach that begins with the operational TRMM precipitation radar algorithm and adjusts its solution only as necessary to simultaneously match the radiometer observations. Where the TRMM precipitation radar (PR) indicates no rain, an optimal estimation procedure using TRMM Microwave Imager (TMI) radiances is used to retrieve nonraining parameters. The optimal estimation methodology ensures that the geophysical parameters are fully consistent with the observed radiances. Within raining fields of view, cloud-resolving model outputs are matched to the liquid and frozen hydrometeor profiles retrieved by the TRMM PR. The profiles constructed in this manner are subsequently used to compute brightness temperatures that are immediately compared to coincident observations from TMI. Adjustments are made to the rainwater and ice concentrations derived by PR in order to achieve agreement at 19 and 85 GHz, vertically polarized brightness temperatures at monthly time scales. The database is generated only in the central 11 pixels of the PR radar scan, and the rain adjustment is performed independently for distinct sea surface temperature (SST) and total precipitable water (TPW) values. Overall, the procedure increases PR rainfall by 4.2%, but the adjustment is not uniform across all SST and TPW regimes. Rainfall differences range from a minimum of −57% for SST of 293 K and TPW of 13 mm to a maximum of +53% for SST of 293 K and TPW of 45 mm. These biases are generally reproduced by a TMI retrieval algorithm that uses the observationally generated database. The algorithm increases rainfall by 5.0% over the PR solution with a minimum of −99% for SST of 293 K and TPW of 14 mm to a maximum of +11.8% for an SST of 294 K and TPW of 50 mm. Some differences are expected because of the algorithm mechanics.

scholarly journals Analysis of the Vertical Air Motions and Raindrop Size Distribution Retrievals of a Squall Line Based on Cloud Radar Doppler Spectral Density Data

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 348
Author(s):  
Ningkun Ma ◽  
Liping Liu ◽  
Yichen Chen ◽  
Yang Zhang
Keyword(s):  
Spectral Density ◽  
Size Distribution ◽  
Squall Line ◽  
Vertical Profiles ◽  
Air Velocity ◽  
Density Data ◽  
Cloud Radar ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Reflectivity Factor

A squall line is a type of strongly organized mesoscale convective system that can cause severe weather disasters. Thus, it is crucial to explore the dynamic structure and hydrometeor distributions in squall lines. This study analyzed a squall line over Guangdong Province on 6 May 2016 that was observed using a Ka-band millimeter-wave cloud radar (CR) and an S-band dual-polarization radar (PR). Doppler spectral density data obtained by the CR were used to retrieve the vertical air motions and raindrop size distribution (DSD). The results showed the following: First, the CR detected detailed vertical profiles and their evolution before and during the squall line passage. In the convection time segment (segment B), heavy rain existed with a reflectivity factor exceeding 35 dBZ and a velocity spectrum width exceeding 1.3 m s−1. In the PR detection, the differential reflectivity factor (Zdr) was 1–2 dB, and the large specific differential phase (Kdp) also represented large liquid water content. In the transition and stratiform cloud time segments (segments B and C), the rain stabilized gradually, with decreasing cloud tops, stable precipitation, and a 0 °C layer bright band. Smaller Kdp values (less than 0.9) were distributed around the 0 °C layer, which may have been caused by the melting of ice crystal particles. Second, from the CR-retrieved vertical air velocity, before squall line passage, downdrafts dominated in local convection and weak updrafts existed in higher-altitude altostratus clouds. In segment B, the updraft air velocity reached more than 8 m s−1 below the 0 °C layer. From segments C to D, the updrafts changed gradually into weak and wide-ranging downdrafts. Third, in the comparison of DSD values retrieved at 1.5 km and DSD values on the ground, the retrieved DSD line was lower than the disdrometer, the overall magnitude of the DSD retrieved was smaller, and the difference decreased from segments C to D. The standardized intercept parameter (Nw) and shape parameter (μ) of the DSD retrieved at 1.8 km showed good agreement with the disdrometer results, and the mass-weighted mean diameter (Dm) was smaller than that on the ground, but very close to the PR-retrieved Dm result at 2 km. Therefore, comparing with the DSD retrieved at around 2 km, the overall number concentration remained unchanged and Dm got larger on the ground, possibly reflecting the process of raindrop coalescence. Lastly, the average vertical profiles of several quantities in all segments showed that, first of all, the decrease of Nw and Dm with height in segments C and D was similar, reflecting the collision effect of falling raindrops. The trends were opposite in segment B, indicating that raindrops underwent intense mixing and rapid collision and growth in this segment. Then, PR-retrieved Dm profiles can verify the rationality of the CR-retrieved Dm. Finally, a vertical velocity profile peak generated a larger Dm especially in segments C and D.

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