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.

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

2005 ◽  
Vol 22 (7) ◽  
pp. 909-929 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Christian D. Kummerow
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Dimensional Structure ◽  
Retrieval Algorithm ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

A Modular Optimal Estimation Method for Combined Radar–Radiometer Precipitation Profiling

2011 ◽  
Vol 50 (2) ◽  
pp. 433-448 ◽  
Author(s):  
S. Joseph Munchak ◽  
Christian D. Kummerow
Keyword(s):  
Size Distribution ◽  
A Priori ◽  
Estimation Method ◽  
Tropical Rainfall Measuring Mission ◽  
Optimal Estimation ◽  
Marshall Islands ◽  
Radar Rainfall ◽  
Brightness Temperatures ◽  
Raindrop Size Distribution ◽  
Combined Algorithm

Abstract Although zonal mean rain rates from the Tropical Rainfall Measuring Mission (TRMM) are in good (<10%) agreement between the TRMM Microwave Imager (TMI) and precipitation radar (PR) rainfall algorithms, significant uncertainties remain in some regions where these estimates differ by as much as 30% over the period of record. Previous comparisons of these algorithms with ground validation (GV) rainfall have shown significant (>10%) biases of differing sign at various GV locations. Reducing these biases is important in the context of developing a database of cloud profiles for passive microwave retrievals that is based upon the PR-measured profiles. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution, and cloud water path (cLWP) are retrieved for each radar profile. The modular nature of the framework provides the opportunity to test the sensitivity of the retrieval to the inclusion of different measurements, retrieved parameters, and models for microwave scattering properties of hydrometeors. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, Florida, shows agreement within 2%, which exceeds previous algorithms’ ability to match rainfall at these two sites. Errors between observed and simulated brightness temperatures are reduced and climatological features of the DSD, as measured by disdrometers at these two locations, are also reproduced in the output of the combined algorithm.

scholarly journals Differences of Rainfall Estimates over Land by Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and TRMM Microwave Imager (TMI)—Dependence on Storm Height

2005 ◽  
Vol 44 (3) ◽  
pp. 367-383 ◽  
Author(s):  
Fumie A. Furuzawa ◽  
Kenji Nakamura
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Local Times ◽  
Precipitation Radar ◽  
Tropical Rainfall ◽  
Convective Rain ◽  
Brightness Temperatures ◽  
Trmm Precipitation ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Orbit Data

Abstract It is well known that precipitation rate estimation is poor over land. Using the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI), the performance of the TMI rain estimation was investigated. Their differences over land were checked by using the orbit-by-orbit data for June 1998, December 1998, January 1999, and February 1999, and the following results were obtained: 1) Rain rate (RR) near the surface for the TMI (TMI-RR) is smaller than that for the PR (PR-RR) in winter; it is also smaller from 0900 to 1800 LT. These dependencies show some variations at various latitudes or local times. 2) When the storm height is low (<5 km), the TMI-RR is smaller than the PR-RR; when it is high (>8 km), the PR-RR is smaller. These dependencies of the RR on the storm height do not depend on local time or latitude. The tendency for a TMI-RR to be smaller when the storm height is low is more noticeable in convective rain than in stratiform rain. 3) Rain with a low storm height predominates in winter or from 0600 to 1500 LT, and convective rain occurs frequently from 1200 to 2100 LT. Result 1 can be explained by results 2 and 3. It can be concluded that the TMI underestimates rain with low storm height over land because of the weakness of the TMI algorithm, especially for convective rain. On the other hand, it is speculated that TMI overestimates rain with high storm height because of the effect of anvil rain with low brightness temperatures at high frequencies without rain near the surface, and because of the effect of evaporation or tilting, which is indicated by a PR profile and does not appear in the TMI profile. Moreover, it was found that the PR rain for the cases with no TMI rain amounted to about 10%–30% of the total but that the TMI rain for the cases with no PR rain accounted for only a few percent of the TMI rain. This result can be explained by the difficulty of detecting shallow rain with the TMI.

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 Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1225
Author(s):  
Lanka Karthikeyan ◽  
Ming Pan ◽  
Dasika Nagesh Kumar ◽  
Eric F. Wood
Keyword(s):  
Soil Moisture ◽  
Global Scale ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Structural Uncertainty ◽  
Three Solutions ◽  
Brightness Temperatures ◽  
Retrieval Algorithms

Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.

scholarly journals The GRAPE aerosol retrieval algorithm

2009 ◽  
Vol 2 (2) ◽  
pp. 679-701 ◽  
Author(s):  
G. E. Thomas ◽  
C. A. Poulsen ◽  
A. M. Sayer ◽  
S. H. Marsh ◽  
S. M. Dean ◽  
...  
Keyword(s):  
A Priori ◽  
Optimal Estimation ◽  
Effective Radius ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Data Set ◽  
Cloud Parameters ◽  
Aerosol Retrieval ◽  
Retrieval Scheme ◽  
Theoretical Performance

Abstract. The aerosol component of the Oxford-Rutherford Aerosol and Cloud (ORAC) combined cloud and aerosol retrieval scheme is described and the theoretical performance of the algorithm is analysed. ORAC is an optimal estimation retrieval scheme for deriving cloud and aerosol properties from measurements made by imaging satellite radiometers and, when applied to cloud free radiances, provides estimates of aerosol optical depth at a wavelength of 550 nm, aerosol effective radius and surface reflectance at 550 nm. The aerosol retrieval component of ORAC has several incarnations – this paper addresses the version which operates in conjunction with the cloud retrieval component of ORAC (described by Watts et al., 1998), as applied in producing the Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) data-set. The algorithm is described in detail and its performance examined. This includes a discussion of errors resulting from the formulation of the forward model, sensitivity of the retrieval to the measurements and a priori constraints, and errors resulting from assumptions made about the atmospheric/surface state.

Impacts of A Priori Databases Using Six WRF Microphysics Schemes on Passive Microwave Rainfall Retrievals

2013 ◽  
Vol 30 (10) ◽  
pp. 2367-2381 ◽  
Author(s):  
Ju-Hye Kim ◽  
Dong-Bin Shin ◽  
Christian Kummerow
Keyword(s):  
A Priori ◽  
Wrf Model ◽  
Tropical Rainfall Measuring Mission ◽  
Cloud Microphysics ◽  
Passive Microwave ◽  
A Priori Information ◽  
Physically Based ◽  
Double Moment ◽  
Priori Information ◽  
The Individual

Abstract Physically based rainfall retrievals from passive microwave sensors often make use of cloud-resolving models (CRMs) to build a priori databases of potential rain structures. Each CRM, however, has its own cloud microphysics assumptions. Hence, approximated microphysics may cause uncertainties in the a priori information resulting in inaccurate rainfall estimates. This study first builds a priori databases by combining the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations and simulations from the Weather Research and Forecasting (WRF) model with six different cloud microphysics schemes. The microphysics schemes include the Purdue–Lin (LIN), WRF Single-Moment 6 (WSM6), Goddard Cumulus Ensemble (GCE), Thompson (THOM), WRF Double-Moment 6 (WDM6), and Morrison (MORR) schemes. As expected, the characteristics of the a priori databases are inherited from the individual cloud microphysics schemes. There are several distinct differences in the databases. Particularly, excessive graupel and snow exist with the LIN and THOM schemes, while more rainwater is incorporated into the a priori information with WDM6 than with any of the other schemes. Major results show that convective rainfall regions are not well captured by the LIN and THOM schemes-based retrievals. Rainfall distributions and their quantities retrieved from the WSM6 and WDM6 schemes-based estimations, however, show relatively better agreement with the PR observations. Based on the comparisons of the various microphysics schemes in the retrievals, it appears that differences in the a priori databases considerably affect the properties of rainfall estimations.

scholarly journals Impact of Misrepresentation of Freezing-Level Height by the TRMM Algorithm on Shallow Rain Statistics over India and Adjoining Oceans

2013 ◽  
Vol 52 (9) ◽  
pp. 2001-2008 ◽  
Author(s):  
K. Saikranthi ◽  
T. Narayana Rao ◽  
B. Radhakrishna ◽  
S. Vijaya Bhaskara Rao
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Precipitation Radar ◽  
Study Region ◽  
Freezing Level ◽  
Northwestern India ◽  
Trmm Precipitation ◽  
Radar Measurements ◽  
The Impact ◽  
The Himalaya ◽  
Level Height

AbstractThe estimation of freezing level-height (FLH) by the Tropical Rainfall Measuring Mission (TRMM) algorithm is evaluated, against several other data sources, over India and adjoining oceans. It is observed that the TRMM algorithm either underestimates or overestimates the FLH [relative to radiosonde- and ECMWF Interim Re-Analysis (ERA)-derived FLH] at latitudes > 20°N over India. The agreement between the FLHs obtained from ERA and radiosonde and the TRMM-derived brightband height suggests that usage of ERA-derived FLH may improve shallow rain statistics. The impact of misrepresentation of FLH by the TRMM algorithm on shallow rain statistics is assessed by using 13 yr of TRMM precipitation radar measurements. It is noted that the misidentification of FLH alone affects (mostly underestimates) the shallow rain occurrence and rain fraction by 3%–8% over the study region. The magnitude of underestimation is large over the southern slopes of the Himalaya, the northern plains, and in northwestern India. TRMM identifies most of the shallow rain (30%–50%) as cold rain in regions where the underestimation of FLH is high. This situation could introduce some error in the correction of reflectivity for attenuation and in the retrieval of latent heat profiles.

Contribution of Tropical Cyclones to Global Very Deep Convection

2014 ◽  
Vol 27 (11) ◽  
pp. 4313-4336 ◽  
Author(s):  
Haiyan Jiang ◽  
Cheng Tao
Keyword(s):  
Tropical Cyclones ◽  
Deep Convection ◽  
Convective Available Potential Energy ◽  
Tropical Rainfall Measuring Mission ◽  
Precipitable Water ◽  
Moisture Exchange ◽  
Derived Properties ◽  
Trmm Precipitation ◽  
The Tropics ◽  
Heat And Moisture

Abstract Based on the 12-yr (1998–2009) Tropical Rainfall Measuring Mission (TRMM) precipitation feature (PF) database, both radar and infrared (IR) observations from TRMM are used to quantify the contribution of tropical cyclones (TCs) to very deep convection (VDC) in the tropics and to compare TRMM-derived properties of VDC in TCs and non-TCs. Using a radar-based definition, it is found that the contribution of TCs to total VDC in the tropics is not much higher than the contribution of TCs to total PFs. However, the area-based contribution of TCs to overshooting convection defined by IR is 13.3%, which is much higher than the 3.2% contribution of TCs to total PFs. This helps explain the contradictory results between previous radar-based and IR-based studies and indicates that TCs only contribute disproportionately large amount of overshooting convection containing mainly small ice particles that are barely detected by the TRMM radar. VDC in non-TCs over land has the highest maximum 30- and 40-dBZ height and the strongest ice-scattering signature derived from microwave 85- and 37-GHz observations, while VDC in TCs has the coldest minimum IR brightness temperature and largest overshooting distance and area. This suggests that convection is much more intense in non-TCs over land but is much deeper or colder in TCs. It is found that VDC in TCs usually has smaller environmental shear but larger total precipitable water and convective available potential energy than those in non-TCs. These findings offer evidence that TCs may contribute disproportionately to troposphere-to-stratosphere heat and moisture exchange.

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