Cross-validation of active and passive microwave snowfall products over the continental United States

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’sCoreObservatorysensors and theCloudSatradar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radarcomposite product over the continental United States during the period from November 2014 to September 2020. Theanalysis includes the Dual-Frequency Precipitation Radar (DPR) retrieval and its single-frequency counterparts, the GPMCombined Radar Radiometer Algorithm (CORRA), theCloudSatSnow Profile product (2C-SNOW-PROFILE), and twopassive microwave retrievals, i.e., the Goddard Profiling algorithm (GPROF) and the Snow Retrieval Algorithm for GMI(SLALOM). The 2C-SNOW retrieval has the highest Heidke skill score (HSS) for detecting snowfall among the productsanalyzed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of thesnow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in theGMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall ratesby a factor of 2 compared to MRMS. Large discrepancies (RMSE of 0.7–1.5 mm h21) between spaceborne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of theremote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by theconfounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers

Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation

This paper introduces a new Bayesian approach to the inverse problem of passive microwave rainfall retrieval. The proposed methodology relies on a regularization technique and makes use of two joint dictionaries of coincidental rainfall profiles and their corresponding upwelling spectral radiative fluxes. A sequential detection-estimation strategy is adopted, which basically assumes that similar rainfall intensity values and their spectral radiances live close to some sufficiently smooth manifolds with analogous local geometry. The detection step employs a nearest neighborhood classification rule, while the estimation scheme is equipped with a constrained shrinkage estimator to ensure stability of retrieval and some physical consistency. The algorithm is examined using coincidental observations of the active precipitation radar (PR) and passive microwave imager (TMI) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. We present promising results of instantaneous rainfall retrieval for some tropical storms and mesoscale convective systems over ocean, land, and coastal zones. We provide evidence that the algorithm is capable of properly capturing different storm morphologies including high intensity rain-cells and trailing light rainfall, especially over land and coastal areas. The algorithm is also validated at an annual scale for calendar year 2013 versus the standard (version 7) radar (2A25) and radiometer (2A12) rainfall products of the TRMM satellite

Passive Microwave Precipitation Detection Biases: Relationship to Cloud Properties

Accurate measurement of the Earth’s hydrologic cycle requires a more precise understanding of precipitation accumulation and intensity on a global scale. While there is a long record of passive microwave satellite measurements, passive microwave rainfall retrievals often fail to detect light precipitation or have light rain intensity biases because they cannot differentiate between emission from cloud and rain water. Previous studies have shown that AMSR-E significantly underestimates rainfall occurrence and volume compared to CloudSat. This underestimation totals just below 0.6 mm/day quasi-globally (60S-60N), but there are larger regional variations related to the dominant cloud regime. This study aims to use Moderate Resolution Imaging Spectroradiometer (MODIS) and the 94-GHz CloudSat Cloud Profiling Radar (CPR), which has a high sensitivity to light rain, with the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) observations, to help better characterize the properties of clouds that lead to passive microwave rainfall detection biases. CPR cloud and precipitation retrievals, AMSR-E Level-2B Goddard Profiling 2010 Algorithm (GPROF 2010) rainfall retrievals, and MODIS cloud properties were collocated and analyzed for 2007-2009. MODIS cloud microphysical and macrophysical properties, such as optical thickness, particle effective radius, and liquid water path were analyzed when precipitation is detected by CloudSat and missed by AMSR-E. Results are consistent with past studies and show large passive microwave precipitation detection biases compared to CloudSat in stratocumulus and shallow cumulus regimes. An examination of cases where AMSR-E failed to detect precipitation detected by CloudSat shows that warm rain detection biases occur more frequently within lower LWP, τ , and CTH bins, but biases at higher LWP, τ_ , and CTH contribute more to the total frequency of missed precipitation. Warm rain detection biases occur more frequently and biases contribute to more of the total frequency of missed precipitation for rve > 16 µ-m. Cloud property-dependent thresholds were calculated and compared against Advanced Microwave Scanning Radiometer (Earth Observing System) (AMSR-E) Goddard Profiling Algorithm (GPROF). All cloud property-dependent brightness temperature (TB) thresholds showed improvements in hit rate and volumetric hit rates. Cloud property-dependent TB thresholds were investigated to determine if thresholds can be improved by separately constraining data to environmental and cloud regimes. Descent and stratocumulus regimes, which generally consist of warm clouds, showed further improvements of warm rain detection. Results suggest that aprori knowledge of cloud property information and environmental information could significantly improve the detection of warm precipitation in GPROF retrievals

Global Precipitation Measurement (GPM): Unified Precipitation Estimation From Space

Global Precipitation Measurement (GPM) is an international satellite mission that uses measurements from an advanced radar/radiometer system on a Core Observatory as reference standards to unify and advance precipitation estimates through a constellation of research and operational microwave sensors. GPM is a science mission focusing on a key component of the Earth's water and energy cycle, delivering near real-time observations of precipitation for monitoring severe weather events, freshwater resources, and other societal applications. This work presents the GPM mission design, together with descriptions of sensor characteristics, inter-satellite calibration, retrieval methodologies, ground validation activities, and societal applications

Spectrum Synergy for Investigating Cloud Microphysics

Observations from spaceborne microwave (MW) and infrared (IR) passive sensors are the backbone of current satellite meteorology, essential for data assimilation into modern numerical weather prediction and for climate benchmarking. While MW and IR observations from space offer complementary features with respect to cloud properties, their synergy for cloud investigation is currently underexplored, despite the presence of both MW and IR sensors on operational meteorological satellites such as the EUMETSAT Polar System (EPS) MetOp series. As such, several key cloud microphysical properties are not part of the operational products available from EPS MetOp sensors. In addition, the EPS Second Generation (EPS-SG) series, scheduled for launch starting from 2024 onward, will carry sensors such as the Microwave Sounder (MWS) and IASI Next Generation (IASI-NG), enhancing spatial and spectral resolutions and thus capacity to retrieve cloud properties. This article presents the Combined MWS and IASI-NG Soundings for Cloud Properties (ComboCloud) project, funded by EUMETSAT with the overall objective to specify, prototype, and validate algorithms for the retrieval of cloud microphysical properties (e.g., water content and drop effective radius) from the synergy of passive MW and IR observations. The article presents the synergy rationale, the algorithm design, and the results obtained exploiting simulated observations from EPS and EPS-SG sensors, quantifying the benefits to be expected from the MW-IR synergy and the new generation sensors

Global Precipitation Measurement

This chapter begins with a brief history and background of microwave precipitation sensors, with a discussion of the sensitivity of both passive and active instruments, to trace the evolution of satellite-based rainfall techniques from an era of inference to an era of physical measurement. Next, the highly successful Tropical Rainfall Measuring Mission will be described, followed by the goals and plans for the Global Precipitation Measurement (GPM) Mission and the status of precipitation retrieval algorithm development. The chapter concludes with a summary of the need for space-based precipitation measurement, current technological capabilities, near-term algorithm advancements and anticipated new sciences and societal benefits in the GPM era

Method to combine spaceborne radar and radiometric observations of precipitation, A

2010 Fall.Includes bibliographical references.This dissertation describes the development and application of a combined radar-radiometer rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) satellite. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution (PSD), and cloud water path (cLWP) are retrieved for each radar profile. 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, FL shows agreement within 2% which exceeds previous algorithms' ability to match rainfall at these two sites. The algorithm is then applied to two years of TRMM data over oceans to determine the sources of DSD variability. Three correlated sets of variables representing storm dynamics, background environment, and cloud microphysics are found to account for approximately 50% of the variability in the absolute and reflectivity-normalized median drop size. Structures of radar reflectivity are also identified and related to drop size, with these relationships being confirmed by ground-based polarimetric radar data from the North American Monsoon Experiment (NAME). Regional patterns of DSD and the sources of variability identified herein are also shown to be consistent with previous work documenting regional DSD properties. In particular, mid-latitude regions and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Intertropical Convergence Zone. Due to properties of the DSD and rain water/cloud water partitioning that change with column water vapor, it is shown that increases in water vapor in a global warming scenario could lead to slight (1%) underestimates of a rainfall trends by radar but larger overestimates (5%) by radiometer algorithms. Further analyses are performed to compare tropical oceanic mean rainfall rates between the combined algorithm and other sources. The combined algorithm is 15% higher than the version 6 of the 2A25 radar-only algorithm and 6.6% higher than the Global Precipitation Climatology Project (GPCP) estimate for the same time-space domain. Despite being higher than these two sources, the combined total is not inconsistent with estimates of the other components of the energy budget given their uncertainties

Investigation of passive atmospheric sounding using millimeter and submillimeter wavelength channels

Activities within the period from July 1, 1992 through December 31, 1992 by Georgia Tech researchers in millimeter and submillimeter wavelength tropospheric remote sensing have been centered around the calibration of the Millimeter-wave Imaging Radiometer (MIR), preliminary flight data analysis, and preparation for TOGA/COARE. The MIR instrument is a joint project between NASA/GSFC and Georgia Tech. In the current configuration, the MIR has channels at 90, 150, 183(+/-1,3,7), and 220 GHz. Provisions for three additional channels at 325(+/-1,3) and 8 GHz have been made, and a 325-GHz receiver is currently being built by the ZAX Millimeter Wave Corporation for use in the MIR. Past Georgia Tech contributions to the MIR and its related scientific uses have included basic system design studies, performance analyses, and circuit and radiometric load design, in-flight software, and post-flight data display software. The combination of the above millimeter wave and submillimeter wave channels aboard a single well-calibrated instrument will provide unique radiometric data for radiative transfer and cloud and water vapor retrieval studies. A paper by the PI discussing the potential benefits of passive millimeter and submillimeter wave observations for cloud, water vapor and precipitation measurements has recently been published, and is included as an appendix

Parametric optimal estimation retrieval of the non-precipitating parameters over the global oceans, A

2006 Summer.Includes bibliographical references (pages 82-87).Covers not scanned.Print version deaccessioned 2021.There are a multitude of spacebome microwave sensors in orbit, including the TRMM Microwave Imager (TMI), the Special Sensor Microwave/lmager (SSM/I) onboard the DMSP satellites, the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), SSMIS, WINDSAT, and others. Future missions, such as the planned Global Precipitation Measurement (GPM) Mission, will incorporate additional spacebome microwave sensors. The need for consistent geophysical parameter retrievals among an ever-increasing number of microwave sensors requires the development of a physical retrieval scheme independent of any particular sensor and flexible enough so that future microwave sensors can be added with relative ease. To this end, we attempt to develop a parametric retrieval algorithm currently applicable to the non-precipitating atmosphere with the goal of having consistent non-precipitating geophysical parameter products. An algorithm of this nature makes is easier to merge separate products, which, when combined, would allow for additional global sampling or longer time series of the retrieved global geophysical parameters for climate purposes. This algorithm is currently applied to TMI, SSM/I and AMSR-E with results that are comparable to other independent microwave retrievals of the non-precipitating parameters designed for specific sensors. The physical retrieval is developed within the optimal estimation framework. The development of the retrieval within this framework ensures that the simulated radiances corresponding to the retrieved geophysical parameters will always agree with observed radiances regardless of the sensor being used. Furthermore, a framework of this nature allows one to easily add additional physics to describe radiation propagation through raining scenes, thus allowing for the merger of cloud and precipitation retrievals, if so desired. Additionally, optimal estimation provides error estimates on the retrieval, a product often not available in other algorithms, information on potential forward model/sensor biases, and a number of useful diagnostics providing information on the validity and significance of the retrieval (such as Chi-Square, indicative of the general "fit" between the model and observations and the A-Matrix, indicating the sensitivity of the model to a change in the geophysical parameters). There is an expected global response of these diagnostics based on the scene being observed, such as in the case of a raining scene. Fortunately, since TRMM has a precipitation radar (TRMM PR) in addition to a radiometer (TMI) flying on-board, the expected response of the retrieval diagnostics to rainfall can be evaluated. It is shown that a potentially powerful rainfall screen can then be developed for use in passive microwave rainfall and cloud property retrieval algorithms with the possibility of discriminating between precipitating and nonprecipitating scenes, and further indicating the possible contamination of rainfall in cloud liquid water path microwave retrievals

SLALOM: An all-surface snow water path retrieval algorithm for the GPM microwave imager

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI