scholarly journals Quantifying Global Uncertainties in a Simple Microwave Rainfall Algorithm

2006 ◽  
Vol 23 (1) ◽  
pp. 23-37 ◽  
Author(s):  
Christian Kummerow ◽  
Wesley Berg ◽  
Jody Thomas-Stahle ◽  
Hirohiko Masunaga
Keyword(s):  
A Priori ◽  
Simple Algorithm ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Global Scale ◽  
Time And Space ◽  
Validation Data ◽  
Brightness Temperatures ◽  
Time Domains ◽  
Imperfect Knowledge

Abstract While a large number of methods exist in the literature for retrieving rainfall from passive microwave brightness temperatures, little has been written about the quantitative assessment of the expected uncertainties in these rainfall products at various time and space scales. The latter is the result of two factors: sparse validation sites over most of the world’s oceans, and algorithm sensitivities to rainfall regimes that cause inconsistencies against validation data collected at different locations. To make progress in this area, a simple probabilistic algorithm is developed. The algorithm uses an a priori database constructed from the Tropical Rainfall Measuring Mission (TRMM) radar data coupled with radiative transfer computations. Unlike efforts designed to improve rainfall products, this algorithm takes a step backward in order to focus on uncertainties. In addition to inversion uncertainties, the construction of the algorithm allows errors resulting from incorrect databases, incomplete databases, and time- and space-varying databases to be examined. These are quantified. Results show that the simple algorithm reduces errors introduced by imperfect knowledge of precipitation radar (PR) rain by a factor of 4 relative to an algorithm that is tuned to the PR rainfall. Database completeness does not introduce any additional uncertainty at the global scale, while climatologically distinct space/time domains add approximately 25% uncertainty that cannot be detected by a radiometer alone. Of this value, 20% is attributed to changes in cloud morphology and microphysics, while 5% is a result of changes in the rain/no-rain thresholds. All but 2%–3% of this variability can be accounted for by considering the implicit assumptions in the algorithm. Additional uncertainties introduced by the details of the algorithm formulation are not quantified in this study because of the need for independent measurements that are beyond the scope of this paper. A validation strategy for these errors is outlined.

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.

scholarly journals Persistent Nature of Secondary Diurnal Modes of Precipitation over Oceanic and Continental Regimes

2008 ◽  
Vol 21 (16) ◽  
pp. 4115-4131 ◽  
Author(s):  
Song Yang ◽  
Kwo-Sen Kuo ◽  
Eric A. Smith
Keyword(s):  
Microwave Radiometer ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Early Morning ◽  
Global Scale ◽  
Fourier Decomposition ◽  
Identification Scheme ◽  
Fourier Harmonic ◽  
Clear Cut ◽  
Physically Based

Abstract This investigation seeks a better understanding of the assorted mechanisms controlling the global distribution of diurnal precipitation variability based on the use of the Tropical Rainfall Measuring Mission (TRMM) microwave radiometer and radar data. The horizontal distributions of precipitation’s diurnal cycle are derived from 8 yr of TRMM Microwave Imager (TMI) and Precipitation Radar (PR) measurements involving three TRMM standard rain retrieval algorithms; the resultant distributions are analyzed at various spatiotemporal scales. The results reveal both the prominent and expected late-evening (LE) to early-morning (EM) precipitation maxima over oceans and the counterpart prominent and expected mid- to late-afternoon (MLA) maxima over continents. Moreover, and not generally recognized, the results reveal a widespread distribution of secondary maxima, which generally mirror their counterpart regime’s behavior, occurring over both oceans and continents. That is, many ocean regions exhibit clear-cut secondary MLA precipitation maxima, while many continental regions exhibit just as evident secondary LE–EM maxima. This investigation is the first comprehensive study of these globally prevalent secondary maxima and their widespread nature, a type of study only made possible when the analysis procedure is applied to a high-quality global-scale precipitation dataset. The characteristics of the secondary maxima are mapped and described on global grids using an innovative clock-face format, while a current study that is to be published at a later date provides physically based explanations of the seasonal regional distributions of the secondary maxima. In addition to a primary “explicit” maxima identification scheme, a secondary “Fourier decomposition” maxima identification scheme is used as a cross-check to examine the amplitude and phase properties of the multimodal maxima. Accordingly, the advantages and ambiguities resulting from the use of a Fourier harmonic analysis are investigated.

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 Availability of High-Quality TRMM Ground Validation Data from Kwajalein, RMI: A Practical Application of the Relative Calibration Adjustment Technique

2009 ◽  
Vol 26 (3) ◽  
pp. 413-429 ◽  
Author(s):  
David A. Marks ◽  
David B. Wolff ◽  
David S. Silberstein ◽  
Ali Tokay ◽  
Jason L. Pippitt ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Marshall Islands ◽  
Practical Application ◽  
High Quality ◽  
Validation Data ◽  
Tropical Ocean ◽  
Nasa Goddard Space ◽  
Ground Validation ◽  
Improved Stability

Abstract Since the Tropical Rainfall Measuring Mission (TRMM) satellite launch in November 1997, the TRMM Satellite Validation Office (TSVO) at NASA Goddard Space Flight Center (GSFC) has been performing quality control and estimating rainfall from the KPOL S-band radar at Kwajalein, Republic of the Marshall Islands. Over this period, KPOL has incurred many episodes of calibration and antenna pointing angle uncertainty. To address these issues, the TSVO has applied the relative calibration adjustment (RCA) technique to eight years of KPOL radar data to produce Ground Validation (GV) version 7 products. This application has significantly improved stability in KPOL reflectivity distributions needed for probability matching method (PMM) rain-rate estimation and for comparisons to the TRMM precipitation radar (PR). In years with significant calibration and angle corrections, the statistical improvement in PMM distributions is dramatic. The intent of this paper is to show improved stability in corrected KPOL reflectivity distributions by using the PR as a stable reference. Intermonth fluctuations in mean reflectivity differences between the PR and corrected KPOL are on the order of ±1–2 dB, and interyear mean reflectivity differences fluctuate by approximately ±1 dB. This represents a marked improvement in stability with confidence comparable to the established calibration and uncertainty boundaries of the PR. The practical application of the RCA method has salvaged eight years of radar data that would have otherwise been unusable and has made possible a high-quality database of tropical ocean–based reflectivity measurements and precipitation estimates for the research community.

scholarly journals Toward Improving Ice Water Content and Snow-Rate Retrievals from Radars. Part II: Results from Three Wavelength Radar–Collocated In Situ Measurements and CloudSat–GPM–TRMM Radar Data

2018 ◽  
Vol 57 (2) ◽  
pp. 365-389 ◽  
Author(s):  
Andrew Heymsfield ◽  
Aaron Bansemer ◽  
Norman B. Wood ◽  
Guosheng Liu ◽  
Simone Tanelli ◽  
...  
Keyword(s):  
Mass Flux ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Validation Data ◽  
Precipitation Measurement ◽  
Ice Water Content ◽  
Global Precipitation Measurement ◽  
Ice Water ◽  
Global Precipitation

AbstractTwo methods for deriving relationships between the equivalent radar reflectivity factor Ze and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop Ze–S relationships. In the second method, measurements of Ze at the top of the melting layer (ML), from radars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The Ze–S relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standard satellite products and with a number of relationships developed and reported by others. It is shown that there are substantial differences among them. The relationships developed here promise improvements in snowfall-rate retrievals from satellite-based radar measurements.

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 Validation of nine years of MOPITT V5 NIR using MOZAIC/IAGOS measurements: biases and long-term stability

2014 ◽  
Vol 7 (11) ◽  
pp. 3783-3799 ◽  
Author(s):  
A. T. J. de Laat ◽  
I. Aben ◽  
M. Deeter ◽  
P. Nédélec ◽  
H. Eskes ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transport Model ◽  
A Priori ◽  
Validation Data ◽  
Chemistry Transport Model ◽  
Large Variability ◽  
Long Term Stability ◽  
Spatio Temporal ◽  
Good Agreement

Abstract. Validation results from a comparison between Measurement Of Pollution In The Troposphere (MOPITT) V5 Near InfraRed (NIR) carbon monoxide (CO) total column measurements and Measurement of Ozone and Water Vapour on Airbus in-service Aircraft (MOZAIC)/In-Service Aircraft for a Global Observing System (IAGOS) aircraft measurements are presented. A good agreement is found between MOPITT and MOZAIC/IAGOS measurements, consistent with results from earlier studies using different validation data and despite large variability in MOPITT CO total columns along the spatial footprint of the MOZAIC/IAGOS measurements. Validation results improve when taking the large spatial footprint of the MOZAIC/IAGOS data into account. No statistically significant drift was detected in the validation results over the period 2002–2010 at global, continental and local (airport) scales. Furthermore, for those situations where MOZAIC/IAGOS measurements differed from the MOPITT a priori, the MOPITT measurements clearly outperformed the MOPITT a priori data, indicating that MOPITT NIR retrievals add value to the MOPITT a priori. Results from a high spatial resolution simulation of the chemistry-transport model MOCAGE (MOdèle de Chimie Atmosphérique à Grande Echelle) showed that the most likely explanation for the large MOPITT variability along the MOZAIC-IAGOS profile flight path is related to spatio-temporal CO variability, which should be kept in mind when using MOZAIC/IAGOS profile measurements for validating satellite nadir observations.

scholarly journals Investigating Hector Convective Development and Microphysical Structure Using High-Resolution Model Simulations, Ground-Based Radar Data, and TRMM Satellite Data

2014 ◽  
Vol 71 (4) ◽  
pp. 1353-1370 ◽  
Author(s):  
Sabrina Gentile ◽  
Rossella Ferretti ◽  
Frank Silvio Marzano
Keyword(s):  
High Resolution ◽  
Conceptual Model ◽  
Vertical Structure ◽  
Mesoscale Model ◽  
Tropical Rainfall Measuring Mission ◽  
Sea Breeze ◽  
Radar Data ◽  
Full Life Cycle ◽  
Resolution Model ◽  
High Resolution Model

Abstract One event of a tropical thunderstorm typically observed in northern Australia, known as Hector, is investigated using high-resolution model output from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) observations from a ground-based weather radar located in Berrimah (Australia) and data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The analysis is carried out by tracking the full life cycle of Hector from prestorm stage to the decaying stage. In both the prestorm stage, characterized by nonprecipitating cells, and the triggering stage, when the Hector storm is effectively initiated, an analysis is performed with the aid of high-spatial-and-temporal-resolution MM5 output and the Berrimah ground-based radar imagery. During the mature (“old”) stage of Hector, considering the conceptual model for tropical convection suggested by R. Houze, TRMM Microwave Imager satellite-based data were added to ground-based radar data to analyze the storm vertical structure (dynamics, thermodynamics, and hydrometeor contents). Model evaluation with respect to observations (radar reflectivity and TRMM data) suggests that MM5 performed fairly well in reproducing the dynamics of Hector, providing support to the assertion that the strength of convection, in terms of vertical velocity, largely contributes to the vertical distribution of hydrometeors. Moreover, the stages of the storm and its vertical structure display good agreement with Houze’s aforementioned conceptual model. Finally, it was found that the most important triggering mechanisms for this Hector event are topography, the sea breeze, and a gust front produced by previous convection.

scholarly journals Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)

2019 ◽  
Vol 36 (5) ◽  
pp. 849-864 ◽  
Author(s):  
Ruanyu Zhang ◽  
Christian D. Kummerow ◽  
David L. Randel ◽  
Paula J. Brown ◽  
Wesley Berg ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
A Priori ◽  
Dual Frequency ◽  
Ancillary Data ◽  
Light Rain ◽  
Brightness Temperatures ◽  
The Mean ◽  
Microwave Imager ◽  
Rain Rates ◽  
Global Precipitation

AbstractThis study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

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.

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