Abstract

A significant part of the uncertainty in satellite-based precipitation products stems from differing assumptions about drop size distributions (DSDs). Satellite radar-based retrieval algorithms rely on DSD assumptions that may be overly simplistic, whereas radiometers further struggle to distinguish cloud water from rain. We utilize the Ocean Rainfall and Ice-phase Precipitation Measurement Network (OceanRAIN), version 1.0, dataset to examine the impact of DSD variability on the ability of satellite measurements to accurately estimate rates of warm rainfall. We use the binned disdrometer counts and a simple model of the atmosphere to simulate observations for three satellite architectures. Two are similar to existing instrument combinations on the GPM Core Observatory and CloudSat, and the third is a theoretical triple-frequency radar–radiometer architecture. Using an optimal estimation framework, we find that the assumed DSD shape can have a large impact on retrieved rain rate. A three-parameter normalized gamma DSD model is sufficient for describing and retrieving the DSDs observed in the OceanRAIN dataset. Assuming simpler single-moment DSD models can lead to significant biases in retrieved rain rate, on the order of 100%. Differing DSD assumptions could thus plausibly explain a large portion of the disagreement in satellite-based precipitation estimates.

Abstract

Satellite all-sky radiances from the Advanced Technology Microwave Sounder (ATMS) are assimilated into the Hurricane Weather Research and Forecasting (HWRF) Model using the hybrid Gridpoint Statistical Interpolation analysis system (GSI). To extend the all-sky capability recently developed for global applications to HWRF, some modifications in HWRF and GSI are facilitated. In particular, total condensate is added as a control variable, and six distinct hydrometeor habits are added as state variables in hybrid GSI within HWRF. That is, clear-sky together with cloudy and precipitation-affected satellite pixels are assimilated using the Community Radiative Transfer Model (CRTM) as a forward operator that includes hydrometeor information and Jacobians with respect to hydrometeor variables. A single case study with the 2014 Atlantic storm Hurricane Cristobal is used to demonstrate the methodology of extending the global all-sky capability to HWRF due to ATMS data availability. Two data assimilation experiments are carried out. One experiment uses the operational configuration and assimilates ATMS radiances under the clear-sky condition, and the other experiment uses the modified HWRF system and assimilates ATMS radiances under the all-sky condition with the inclusion of total condensate update and cycling. Observed and synthetic Geostationary Operational Environmental Satellite (GOES)-13 data along with Global Precipitation Measurement Mission (GPM) Microwave Imager (GMI) data from the two experiments are used to show that the experiment with all-sky ATMS radiances assimilation has cloud signatures that are supported by observations. In contrast, there is lack of clouds in the initial state that led to a noticeable lag of cloud development in the experiment that assimilates clear-sky radiances.

Abstract

Prominent achievements made in addressing global precipitation using satellite passive microwave retrievals are often overshadowed by their performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate precipitation measurements. This is especially true over land, with precipitation estimates being based on an observed mean relationship between high-frequency (e.g., 89 GHz) brightness temperature depression (i.e., the ice-scattering signature) and surface precipitation rate. This indirect relationship between the observed (brightness temperatures) and state (precipitation) vectors often leads to inaccurate estimates, with more pronounced biases (e.g., −30% over the United States) observed during extreme events. This study seeks to mitigate these errors by employing previously established relationships between cloud structures and large-scale environments such as CAPE, wind shear, humidity distribution, and aerosol concentrations to form a stronger relationship between precipitation and the scattering signal. The GPM passive microwave operational precipitation retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. The modified algorithm is allowed to use the large-scale environment to filter out a priori states that do not match the general synoptic condition relevant to the observation and thus reduces the difference between the assumed and observed variability in the ice-to-rain ratio. Using the ground Multi-Radar Multi-Sensor (MRMS) network over the United States, the results demonstrate outstanding potential in improving the accuracy of heavy precipitation over land. It is found that individual synoptic parameters can remove 20%–30% of existing bias and up to 50% when combined, while preserving the overall performance of the algorithm.

Abstract

Summer rainfall characteristics over the Korean Peninsula are examined using six years of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements and surface rain measurements from the densely populated rain gauges spread across South Korea. A comparison of the TMI brightness temperature at 85 GHz with the measured surface rain rate reveals that a significant portion of rainfall over the peninsula occurs at warmer brightness temperatures than would be expected from the Goddard profiling (GPROF) database. By incorporating the locally observed rain characteristics into the GPROF algorithm, efforts are made to test whether locally appropriate hydrometeor profiles may be used to improve the retrieved rainfall. Profiles are obtained by simulating rain cases using the cloud-resolving University of Wisconsin Nonhydrostatic Modeling System (UW-NMS) model and matching the calculated radar reflectivities to TRMM precipitation radar (PR) reflectivities. Selected profiles and the corresponding simulated TMI brightness temperatures (limited in this study to values that are larger than 235 K) are added to the GPROF database to form a modified database that is considered to be more suitable for local application over the Korean Peninsula. The rainfall retrieved from the new database demonstrates that heavy-rainfall events—in particular, those associated with warmer clouds—are better captured by the new algorithm as compared with the official TRMM GPROF version-6 retrievals. The results suggest that a more locally suitable rain retrieval algorithm can be developed if locally representative rain characteristics are included in the GPROF algorithm.

Abstract

The one-dimensional, steady-state melting-layer model developed in Part I of this study is used to calculate both the microphysical and radiative properties of melting precipitation, based upon the computed concentrations of snow and graupel just above the freezing level at applicable horizontal grid points of three-dimensional cloud-resolving model simulations. The modified 3D distributions of precipitation properties serve as input to radiative transfer calculations of upwelling radiances and radar extinction/reflectivities at the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) frequencies, respectively. At the resolution of the cloud-resolving model grids (∼1 km), upwelling radiances generally increase if mixed-phase precipitation is included in the model atmosphere. The magnitude of the increase depends upon the optical thickness of the cloud and precipitation, as well as the scattering characteristics of the mixed-phase particles and ice-phase precipitation aloft. Over the set of cloud-resolving model simulations utilized in this study, maximum radiance increases of 43, 28, 18, and 10 K are simulated at 10.65, 19.35, 37.0, and 85.5 GHz, respectively. The impact of melting on TMI-measured radiances is determined not only by the physics of the melting particles but also by the horizontal extent of the melting precipitation, given that the lower-frequency channels have footprints that extend over tens of kilometers. At TMI resolution, the maximum radiance increases are 16, 15, 12, and 9 K at the same frequencies. Simulated PR extinction and reflectivities in the melting layer can increase dramatically if mixed-phase precipitation is included, a result consistent with previous studies. Maximum increases of 0.46 (−2 dB) in extinction optical depth and 5 dB in reflectivity are simulated based upon the set of cloud-resolving model simulations.

Abstract

Observed and modeled rainfall occurrence from shallow (warm) maritime clouds and their composite statistical relationships with cloud macrophysical properties are analyzed and directly compared. Rain falls from ~25% of warm, single-layered, maritime clouds observed by CloudSat and from ~27% of the analogous warm clouds simulated within a large-domain, fine-resolution radiative–convective equilibrium experiment performed using the Regional Atmospheric Modeling System (RAMS), with its sophisticated bin-emulating bulk microphysical scheme. While the fractional occurrence of observed and simulated warm rainfall is found to increase with both increasing column-integrated liquid water and cloud depth, calculations of rainfall occurrence as a joint function of these two macrophysical quantities suggest that the modeled bulk cloud-to-rainwater conversion process is more efficient than observations indicate—in agreement with previous research. Unexpectedly and in opposition to the model-derived relationship, deeper CloudSat-observed warm clouds with little column water mass are more likely to rain than their corresponding shallow counterparts, despite having lower cloud-mean water contents. Given that these composite relationships were derived from statically identified warm clouds, an attempt is made to quantitatively explore rainfall occurrence within the context of the warm cloud life cycle. Extending a previously established cloud-top buoyancy analysis technique, it is shown that rainfall likelihoods from positively buoyant RAMS-simulated clouds more closely resemble the surprising observed relationships than do those derived from negatively buoyant simulated clouds. This suggests that relative to the depiction of warm clouds within the RAMS output, CloudSat observes higher proportions of positively buoyant, developing warm clouds.

Abstract

Three-dimensional tropical squall-line simulations from the Goddard cumulus ensemble (GCE) model are used as input to radiative computations of upwelling microwave brightness temperatures and radar reflectivities at selected microwave sensor frequencies. These cloud/radiative calculations form the basis of a physical cloud/precipitation profile retrieval method that yields estimates of the expected values of the hydrometeor water contents. Application of the retrieval method to simulated nadir-view observations of the aircraft-borne Advanced Microwave Precipitation Radiometer (AMPR) and NASA ER-2 Doppler radar (EDOP) produce random errors of 23%, 19%, and 53% in instantaneous estimates of integrated precipitating liquid, integrated precipitating ice, and surface rain rate, respectively.

On 5 October 1993, during the Convection and Atmospheric Moisture Experiment (CAMEX), the AMPR and EDOP were used to observe convective systems in the vicinity of the Florida peninsula. Although the AMPR data alone could be used to retrieve cloud and precipitation vertical profiles over the ocean, retrievals of high-resolution vertical precipitation structure and profile information over land required the combination of AMPR and EDOP observations.

No validation data are available for this study; however, the retrieved precipitation distributions from the convective systems are compatible with limited radar climatologies of such systems, as well as being radiometrically consistent with both the AMPR and EDOP observations. In the future, the retrieval method will be adapted to the passive and active microwave measurements from the Tropical Rainfall Measuring Mission (TRMM) satellite sensors.

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

There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud-resolving model simulation of warm, shallow clouds is used to simulate satellite observations from three theoretical satellite architectures—one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beamfilling are very large, with retrieved rain rates negatively (low) biased by as much as 40%–50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% as compared with −28%, a reduced spread of retrieval errors, and an additional 17.5% of cases falling within desired uncertainty limits. The results speak to the need for additional high-resolution modeling simulations of warm rain so as to better characterize the uncertainties in satellite precipitation retrievals.

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.