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.

Abstract

A flexible and physical optimal estimation-based inversion algorithm for retrieving atmospheric water vapor and cloud liquid water path from passive microwave radiometers over the global oceans is presented. The algorithm’s main strength lies in its ability to explicitly account for forward model errors that depend on the Earth incidence angle (EIA) at which a given radiometer measurement is made. Validation of total precipitable water (TPW) retrieved from Microwave Humidity Sounder (MHS) measurements against near-coincident estimates of TPW from SuomiNet GPS ground stations shows that retrieved TPW values agree closely with SuomiNet estimates, and somewhat better than values from the Microwave Integrated Retrieval System that are retrieved from the same MHS instruments. More importantly, it is found that the inclusion of appropriate forward model error assumptions, which are tailored to the EIA and sea surface temperature of the scene being considered, are able to almost entirely eliminate EIA-dependent biases in retrieved TPW. This result holds true across all satellites currently carrying an MHS instrument, despite the fact that only measurements from one satellite are used to estimate forward model errors. The consistency achieved by the retrieval algorithm across all view angles suggests that other inversion algorithms, particularly those for cross-track-scanning radiometers and potential future constellations of small satellites, would benefit from the inclusion of nuanced error assumptions that consider the effect of EIA.

Abstract

Satellite-based oceanic precipitation estimates, particularly those derived from the Global Precipitation Measurement (GPM) satellite and CloudSat, suffer from significant disagreement over regions of the globe where warm rain processes are dominant. GPM estimates of average rain rate tend to be lower than CloudSat estimates, due in part to GPM being less sensitive to shallow and/or light precipitation. Using coincident observations between GPM and CloudSat, we find that the GPM_2BCMB product misses about two-thirds of total accumulated warm rain compared to the CloudSat 2C-RAIN-PROFILE product. This difference becomes much smaller when products are compared at 1000 m above the surface (mitigating surface clutter issues) and when forcing the frequency of rain from CloudSat to match the frequency from GPM (mitigating sensitivity issues). However, even then a gap of about 25% remains. Using an optimal estimation retrieval algorithm on the underlying data, we retrieve a similar result, but find that the remaining difference between the GPM and CloudSat retrieved rain rates can be almost entirely accounted for by inconsistent assumptions about the shape of the drop size distribution (DSD) that are made in the two retrievals. We conclude that DSD assumptions contribute significantly to the relative underestimation of warm rain by GPM compared to CloudSat. Because the choice of DSD model has such a large effect on retrieved rain rates, more work is needed to determine whether the DSD models assumed by either the GPM_2BCMB or 2C-RAIN-PROFILE algorithms are actually appropriate for warm rain.

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.

Abstract

The objective of this paper is to establish a computationally efficient algorithm making use of the combination of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) observations. To set up the TMI algorithm, the retrieval databases developed in Part I served as input for different inversion techniques: multistage regressions and neural networks as well as Bayesian estimators. It was found that both Bayesian and neural network techniques performed equally well against PR estimates if all TMI channels were used. However, not using the 85.5-GHz channels produced consistently better results. This confirms the conclusions from Part I. Generally, regressions performed worse; thus they seem less suited for general application due to the insufficient representation of the nonlinearities of the TB–rain rate relation. It is concluded that the databases represent the most sensitive part of rainfall algorithm development.

Sensor combination was carried out by gridding PR estimates of rain liquid water content to 27 km × 44 km horizontal resolution at the center of gravity of the TMI 10.65-GHz channel weighting function. A liquid water dependent database collects common samples over the narrow swath covered by both TMI and PR. Average calibration functions are calculated, dynamically updated along the satellite track, and applied to the full TMI swath. The behavior of the calibration function was relatively stable. The TMI estimates showed a slight underestimation of rainfall at low rain liquid water contents (<0.1 g m⁻³) as well as at very high rainfall intensities (>0.8 g m⁻³) and excellent agreement in between. The biases were found to not depend on beam filling with a strong correlation to rain liquid water for stratiform clouds that may point to melting layer effects.

The remaining standard deviations between instantaneous TMI and PR estimates after calibration may be treated as a total retrieval error, assuming the PR estimates are unbiased. The error characteristics showed a rather constant absolute error of <0.05 g m⁻³ for rain liquid water contents <0.1 g m⁻³. Above, the error increases to 0.6 g m⁻³ for amounts up to 1 g m⁻³. In terms of relative errors, this corresponds to a sharp decrease from >100% to 35% between 0.05 and 0.5 g m⁻³. The database ambiguity, that is, the standard deviation of near-surface rain liquid water contents with the same radiometric signature, provides a means to estimate the contribution from the simulations to this error. In the range where brightness temperatures respond most sensitively to rainwater contents, almost the entire error originates from the ambiguity of signatures. At very low and very high rain rates (<0.05 and >0.7 g m⁻³) at least half of the total error is explained by the inversion process.

Abstract

The 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.

Abstract

The scattering of microwaves at frequencies between 50 and 200 GHz by ice particles in the atmosphere is an essential element in the retrieval of instantaneous surface precipitation from spaceborne passive radiometers. This paper explores how the variable distribution of solid and liquid hydrometeors in the atmospheric column over land surfaces affects the brightness temperature (TB) measured by GMI at 89 GHz through the analysis of Dual-Frequency Precipitation Radar (DPR) reflectivity profiles along the 89-GHz beam. The objective is to refine the statistical relations between observed TBs and surface precipitation over land and to define their limits. As GMI is scanning with a 53° Earth incident angle, the observed atmospheric volume is actually not a vertical column, which may lead to very heterogeneous and seemingly inconsistent distributions of the hydrometeors inside the beam. It is found that the 89-GHz TB is mostly sensitive to the presence of ice hydrometeors several kilometers above the 0°C isotherm, up to 10 km above the 0°C isotherm for the deepest convective systems, but is a modest predictor of the surface precipitation rate. To perform a precise mapping of atmospheric ice, the altitude of the individual ice clusters must be known. Indeed, if variations in the altitude of ice are not accounted for, then the high incident angle of GMI causes a horizontal shift (parallax shift) between the estimated position of the ice clusters and their actual position. We show here that the altitude of ice clusters can be derived from the 89-GHz TB itself, allowing for correction of the parallax shift.

Abstract

This 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.

Abstract

Over the tropical oceans, large discrepancies in TRMM passive and active microwave rainfall retrievals become apparent during El Niño–Southern Oscillation (ENSO) events. This manuscript describes the application of defined precipitation regimes to aid the validation of instantaneous rain rates from TRMM using the S-band radar located on the Kwajalein Atoll. Through the evaluation of multiple case studies, biases in rain-rate estimates from the TRMM radar (PR) and radiometer (TMI) are best explained when derived as a function of precipitation organization (e.g., isolated vs organized) and precipitation type (convective vs stratiform). When examining biases at a 1° × 1° scale, large underestimates in both TMI and PR rain rates are associated with predominately convective events in deep isolated regimes, where TMI and PR retrievals are underestimated by 37.8% and 23.4%, respectively. Further, a positive bias of 33.4% is observed in TMI rain rates within organized convective systems containing large stratiform regions. These findings were found to be consistent using additional analysis from the DYNAMO field campaign. When validating at the TMI footprint scale, TMI–PR differences are driven by stratiform rainfall variability in organized regimes; TMI overestimates this stratiform precipitation by 92.3%. Discrepancies between TMI and PR during El Niño events are related to a shift toward more organized convective systems and derived TRMM rain-rate bias estimates are able to explain 70% of TMI–PR differences during El Niño periods. An extension of the results to passive microwave retrievals reveals issues in discriminating convective and stratiform rainfall within the TMI field of view (FOV), and significant reductions in bias are found when convective fraction is constrained within the Bayesian retrieval.

Abstract

The rapid development of miniaturized satellite instrument technology has created a new opportunity to deploy constellations of passive microwave (PMW) radiometers to permit retrievals of the same Earth scene with very high temporal resolution to monitor cloud evolution and processes. For such a concept to be feasible, it must be shown that it is possible to distinguish actual changes in the atmospheric state from the variability induced by making observations at different Earth incidence angles (EIAs). To this end, we present a flexible and physical optimal estimation-based algorithm designed to retrieve profiles of atmospheric water vapor, cloud liquid water path, and cloud ice water path from cross-track PMW sounders. The algorithm is able to explicitly account for the dependence of forward model errors on EIA and atmospheric regime. When the algorithm is applied to data from the Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) CubeSat mission, its retrieved products are generally in agreement with those obtained from the similar but larger Microwave Humidity Sounder instrument. More importantly, when forward model brightness temperature offsets and assumed error covariances are allowed to change with EIA and sea surface temperature, view-angle-related biases are greatly reduced. This finding is confirmed in two ways: through a comparison with reanalysis data and by making use of brief periods in early 2019 during which the TEMPEST-D spacecraft was rotated such that its scan pattern was along track, allowing dozens of separate observations of any given atmospheric feature along the satellite’s ground track.