scholarly journals Critical Assessment of Microphysical Assumptions within TRMM Radiometer Rain Profile Algorithm Using Satellite, Aircraft, and Surface Datasets from KWAJEX

2006 ◽  
Vol 45 (5) ◽  
pp. 754-786 ◽  
Author(s):  
Steven T. Fiorino ◽  
Eric A. Smith
Keyword(s):  
Rain Rate ◽  
Cloud Model ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Mixed Phase ◽  
Retrieval Algorithm ◽  
Phase Layer ◽  
High Emission ◽  
Rain Rates

Abstract The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager precipitation profile retrieval algorithm (2a12) assumes cloud model–derived vertically distributed microphysics as part of the radiative transfer–controlled inversion process to generate rain-rate estimates. Although this algorithm has been extensively evaluated, none of the evaluation approaches has explicitly examined the underlying microphysical assumptions through a direct intercomparison of the assumed cloud-model microphysics with in situ, three-dimensional microphysical observations. The main scientific objective of this study is to identify and overcome the foremost model-generated microphysical weaknesses in the TRMM 2a12 algorithm through analysis of (a) in situ aircraft microphysical observations; (b) aircraft- and satellite-based passive microwave measurements; (c) ground-, aircraft-, and satellite-based radar measurements; (d) synthesized satellite brightness temperatures and radar reflectivities; (e) radiometer-only profile algorithm retrievals; and (f) radar-only profile or volume algorithm retrievals. Results indicate the assumed 2a12 microphysics differs most from aircraft-observed microphysics where either ground or aircraft radar–derived rain rates exhibit the greatest differences with 2a12-retrieved rain rates. An emission–scattering coordinate system highlights the 2a12 algorithm's tendency to match high-emission/high-scattering observed profiles to high-emission/low-scattering database profiles. This is due to a lack of mixed-phase-layer ice hydrometeor scatterers in the cloud model–generated profiles as compared with observed profiles. Direct comparisons between aircraft-measured and model-generated 2a12 microphysics suggest that, on average, the radiometer algorithm's microphysics database retrieves liquid and ice water contents that are approximately 1/3 the size of those observed at levels below 10 km. Also, the 2a12 rain-rate retrievals are shown to be strongly influenced by the 2a12's convective fraction specification. A proposed modification of this factor would improve 2a12 rain-rate retrievals; however, fundamental changes to the cloud radiation model's ice parameterization are necessary to overcome the algorithm's tendency to produce mixed-phase-layer ice hydrometeor deficits.

scholarly journals A Comparison of Gamma and Lognormal Distributions for Characterizing Satellite Rain Rates from the Tropical Rainfall Measuring Mission

2004 ◽  
Vol 43 (11) ◽  
pp. 1586-1597 ◽  
Author(s):  
Hye-Kyung Cho ◽  
Kenneth P. Bowman ◽  
Gerald R. North
Keyword(s):  
Gamma Distribution ◽  
Lognormal Distribution ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rainfall Data ◽  
Retrieval Algorithm ◽  
Sample Mean ◽  
Tropical Rainfall ◽  
Lognormal Distributions ◽  
Rain Rates

Abstract This study investigates the spatial characteristics of nonzero rain rates to develop a probability density function (PDF) model of precipitation using rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The minimum χ2 method is used to find a good estimator for the rain-rate distribution between the gamma and lognormal distributions, which are popularly used in the simulation of the rain-rate PDF. Results are sensitive to the choice of dynamic range, but both the gamma and lognormal distributions match well with the PDF of rainfall data. Comparison with sample means shows that the parametric mean from the lognormal distribution overestimates the sample mean, whereas the gamma distribution underestimates it. These differences are caused by the inflated tail in the lognormal distribution and the small shape parameter in the gamma distribution. If shape constraint is given, the difference between the sample mean and the parametric mean from the fitted gamma distribution decreases significantly, although the resulting χ2 values slightly increase. Of interest is that a consistent regional preference between two test functions is found. The gamma fits outperform the lognormal fits in wet regions, whereas the lognormal fits are better than the gamma fits for dry regions. Results can be improved with a specific model assumption depending on mean rain rates, but the results presented in this study can be easily applied to develop the rainfall retrieval algorithm and to find the proper statistics in the rainfall data.

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 Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

2008 ◽  
Vol 25 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jianxin Wang ◽  
Brad L. Fisher ◽  
David B. Wolff
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Time Shift ◽  
Estimation Errors ◽  
Rate Estimation ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Event Definition

Abstract This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

scholarly journals Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information

2019 ◽  
Vol 20 (5) ◽  
pp. 1015-1026 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
Hyungjun Kim ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Surface Rain Rate ◽  
Trmm Precipitation ◽  
Time Lagged ◽  
Rain Rates

Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

scholarly journals Possible Effects of Collisional Breakup on Mixed-Phase Deep Convection Simulated by a Spectral (Bin) Cloud Model

2005 ◽  
Vol 62 (6) ◽  
pp. 1917-1931 ◽  
Author(s):  
Axel Seifert ◽  
Alexander Khain ◽  
Ulrich Blahak ◽  
Klaus D. Beheng
Keyword(s):  
Cloud Model ◽  
Deep Convection ◽  
Sensitivity Study ◽  
Mixed Phase ◽  
Convective Clouds ◽  
Hebrew University ◽  
Raindrop Size ◽  
Convective Cells ◽  
Rain Rates ◽  
Rain Formation

Abstract The effects of the collisional breakup of raindrops are investigated using the Hebrew University Cloud Model (HUCM). The parameterizations, which are combined in the new breakup scheme, are those of Low and List, Beard and Ochs, as well as Brown. A sensitivity study reveals strong effects of collisional breakup on the precipitation formation in mixed-phase deep convective clouds for strong as well as for weak precipitation events. Collisional breakup reduces the number of large raindrops, increases the number of small raindrops, and, as a consequence, decreases surface rain rates and considerably reduces the speed of rain formation. In addition, it was found that including breakup can lead to a more intense triggering of secondary convective cells. But a statistical comparison with observed raindrop size distributions shows that the parameterizations might systematically overestimate collisional breakup.

scholarly journals Hurricane GPROF: An Optimized Ocean Microwave Rainfall Retrieval for Tropical Cyclones

2016 ◽  
Vol 33 (7) ◽  
pp. 1539-1556 ◽  
Author(s):  
Paula J. Brown ◽  
Christian D. Kummerow ◽  
David L. Randel
Keyword(s):  
Tropical Cyclones ◽  
Vertical Profile ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Initial Assessment ◽  
Trmm Precipitation ◽  
Microwave Imager ◽  
One Year ◽  
Trmm Microwave Imager ◽  
Rain Rates

AbstractThe Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.

Precipitation nowcasting using spatiotemporal models and volumetric radar data

2020 ◽  
Author(s):  
Seppo Pulkkinen ◽  
Chandrasekaran Venkatachalam ◽  
Annakaisa von Lerber
Keyword(s):  
Parameter Estimation ◽  
Process Model ◽  
Rain Rate ◽  
Temporal Evolution ◽  
Gaussian Function ◽  
Three Dimensional ◽  
Time Step ◽  
Melting Layer ◽  
Spatial Coverage ◽  
Rain Rates

<p>Nowcasts (short-range forecasts) of rainfall can be used for providing early warning of flash floods. Thus, they are of high societal importance especially in densely populated urban areas. Weather radars are ideally suited for this purpose due to their good spatial coverage and high spatial and temporal resolution (e.g. 1 km and 5 minutes).</p><p>A novel approach to radar-based rainfall nowcasting is proposed. The forecast model consists of two components: horizontal advection and temporal evolution of rainfall intensities. The advection velocities are estimated from radar-measured rain rate fields using a pattern matching method. A smooth advection field is obtained by interpolating the motion to areas with no precipitation. The extrapolation is done using a semi-Lagrangian scheme.</p><p>The temporal evolution of rainfall intensities is described in Lagrangian coordinates by using a spatiotemporal process model. Such models are ubiquitous in environmental and physical sciences. This study presents the first attempt to apply such a model to three-dimensional rainfall measurements to capture the vertical structure of rainfall processes. This is done by using a linear integro-differential equation with the Markovian assumption (i.e. the next time step depends conditionally on the previous one). Spatial dependencies are modeled via a convolution kernel. To reduce the dimensionality of the parameter estimation, the kernel is parametrized by a trivariate Gaussian function, and the model is formulated and implemented in the Fourier domain. Finally, the parameter estimation is done in the Bayesian framework by applying a Markov Chain Monte Carlo (MCMC) method with Gibbs sampling.</p><p>The operational feasibility of the proposed model is evaluated by using data from the NEXRAD WSR-88D radar deployed in Fort Worth, Texas. Measurements from 14 elevation angles are used by restricting the analyses to liquid precipitation below the melting layer. The data processing chain consists of 1) temporal interpolation within radar volumes, 2) clutter filtering, 3) attenuation correction, 4) melting layer detection, 5) polarimetric rain rate estimation based on reflectivity, specific differential phase and differential reflectivity and 6) interpolation to a three-dimensional grid.</p><p>The focus of the validation is on higher rain rates (> 5 mm/h) using 10 events during 2018-2019 with mixed convective and stratiform rainfall. Predicted rain rates from the nowcasting model are compared to observations from low-angle radar scans. Using standard verification scores (e.g. equitable threat score and mean absolute error), it is shown that for rainfall rates between 5-25 mm/h, the proposed method can yield up to 30% improvement compared to state of the art extrapolation nowcasting methods. This is attributed to using the spatiotemporal model and vertical profile information obtained from three-dimensional input data.</p>

scholarly journals Optimal rain rate estimation algorithm for light and heavy rain using polarimetric measurements

2011 ◽  
Vol 11 (11) ◽  
pp. 3067-3079 ◽  
Author(s):  
A. Elmzoughi ◽  
R. Abdelfattah ◽  
V. Santalla Del Rio ◽  
Z. Belhadj
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Estimation Algorithm ◽  
Radar Data ◽  
Accurate Estimation ◽  
Rate Estimation ◽  
Rayleigh Approximation ◽  
Physically Based ◽  
Rain Rates

Abstract. In this paper, we propose an ameliorated physically-based rain rate estimation algorithm for semi-arid regions using the Rayleigh approximation. The proposed algorithm simultaneously uses the reflectivity and the specific differential phase to provide an accurate estimation for both small and large rain rates. In order to validate the proposed estimator, simulated polarimetric rain rate data based on a dual approach, referring to both physical and statistical models of the rain target, are used. Moreover, experimental radar data (the same as used in Matrosov et al., 2006) taken in light to moderate stratiform rainfalls with rain rates varying between 2 and 15 mm h−1 were collected as part of the GPM pilot experiment. It is shown that the proposed algorithm for rain rate estimation based on the full set of polarimetric radar measurements agree better with in situ disdrometer ones.

scholarly journals Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain-Rate Estimates at Different Spatial Scales

2008 ◽  
Vol 47 (8) ◽  
pp. 2215-2237 ◽  
Author(s):  
David B. Wolff ◽  
Brad L. Fisher
Keyword(s):  
Rain Rate ◽  
Spatial Scales ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Global Ocean ◽  
Ground Validation ◽  
Microwave Imager ◽  
Rain Events ◽  
Trmm Microwave Imager ◽  
Rain Rates

Abstract This study provides a comprehensive intercomparison of instantaneous rain rates observed by the two rain sensors aboard the Tropical Rainfall Measuring Mission (TRMM) satellite with ground data from two regional sites established for long-term ground validation: Kwajalein Atoll and Melbourne, Florida. The satellite rain algorithms utilize remote observations of precipitation collected by the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard level II rain products are generated from operational applications of the TMI, PR, and combined (COM) rain algorithms using rain information collected from the TMI and the PR along the orbital track of the TRMM satellite. In the first part of the study, 0.5° × 0.5° instantaneous rain rates obtained from the TRMM 3G68 product were analyzed and compared to instantaneous Ground Validation (GV) program rain rates gridded at a scale of 0.5° × 0.5°. In the second part of the study, TMI, PR, COM, and GV rain rates were spatiotemporally matched and averaged at the scale of the TMI footprint (∼150 km2). This study covered a 6-yr period (1999–2004) and consisted of over 50 000 footprints for each GV site. In the first analysis, the results showed that all of the respective rain-rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm h−1 over the ocean. This mode was noted over ocean areas of Kwajalein and Melbourne and has been observed in TRMM tropical–global ocean areas as well.

Estimates of Tropical Diabatic Heating Profiles: Commonalities and Uncertainties

2010 ◽  
Vol 23 (3) ◽  
pp. 542-558 ◽  
Author(s):  
Samson Hagos ◽  
Chidong Zhang ◽  
Wei-Kuo Tao ◽  
Steve Lang ◽  
Yukari N. Takayabu ◽  
...  
Keyword(s):  
Large Scale ◽  
Diabatic Heating ◽  
Tropical Rainfall Measuring Mission ◽  
Upper Troposphere ◽  
Tropical Oceans ◽  
Field Campaigns ◽  
Heating Profiles ◽  
The Tropics ◽  
Rain Rates

Abstract This study aims to evaluate the consistency and discrepancies in estimates of diabatic heating profiles associated with precipitation based on satellite observations and microphysics and those derived from the thermodynamics of the large-scale environment. It presents a survey of diabatic heating profile estimates from four Tropical Rainfall Measuring Mission (TRMM) products, four global reanalyses, and in situ sounding measurements from eight field campaigns at various tropical locations. Common in most of the estimates are the following: (i) bottom-heavy profiles, ubiquitous over the oceans, are associated with relatively low rain rates, while top-heavy profiles are generally associated with high rain rates; (ii) temporal variability of latent heating profiles is dominated by two modes, a deep mode with a peak in the upper troposphere and a shallow mode with a low-level peak; and (iii) the structure of the deep modes is almost the same in different estimates and different regions in the tropics. The primary uncertainty is in the amount of shallow heating over the tropical oceans, which differs substantially among the estimates.

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