Microwave radiative transfer studies of precipitation

Since the deployment of the DMSP SSM/I microwave imagers in 1987, increased utilization of passive microwave radiometry throughout the 10 - 100 GHz spectrum has occurred for measurement of atmospheric constituents and terrestrial surfaces. Our efforts have focused on observations and analysis of the microwave radiative transfer behavior of precipitating clouds. We have focused particular attention on combining both aircraft and SSM/I radiometer imagery with ground-based multiparameter radar observations. As part of this and the past NASA contract, we have developed a multi-stream, polarized radiative transfer model which incorporates scattering. The model has the capability to be initialized with cloud model output or multiparameter radar products. This model provides the necessary 'link' between the passive microwave radiometer and active microwave radar observations. This unique arrangement has allowed the brightness temperatures (TB) to be compared against quantities such as rainfall, liquid/ice water paths, and the vertical structure of the cloud. Quantification of the amounts of ice and water in precipitating clouds is required for understanding of the global energy balance

A Nested K-Nearest Prognostic Approach for Microwave Precipitation Phase Detection over Snow Cover

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10 percent. In particular, the probability of precipitation detection and its solid phase increases by 11 and 8 percent, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surface

Precipitation from Space: Advancing Earth System Science

Of the three primary sources of spatially contiguous precipitation observations (surface networks, ground-based radar, and satellite-based radar/radiometers), only the last is a viable source over ocean and much of the Earth's land. As recently as 15 years ago, users needing quantitative detail of precipitation on anything under a monthly time scale relied upon products derived from geostationary satellite thermal infrared (IR) indices. The Special Sensor Microwave Imager (SSMI) passive microwave (PMW) imagers originated in 1987 and continue today with the SSMI sounder (SSMIS) sensor. The fortunate longevity of the joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) is providing the environmental science community a nearly unbroken data record (as of April 2012, over 14 years) of tropical and sub-tropical precipitation processes. TRMM was originally conceived in the mid-1980s as a climate mission with relatively modest goals, including monthly averaged precipitation. TRMM data were quickly exploited for model data assimilation and, beginning in 1999 with the availability of near real time data, for tropical cyclone warnings. To overcome the intermittently spaced revisit from these and other low Earth-orbiting satellites, many methods to merge PMW-based precipitation data and geostationary satellite observations have been developed, such as the TRMM Multisatellite Precipitation Product and the Climate Prediction Center (CPC) morphing method (CMORPH. The purpose of this article is not to provide a survey or assessment of these and other satellite-based precipitation datasets, which are well summarized in several recent articles. Rather, the intent is to demonstrate how the availability and continuity of satellite-based precipitation data records is transforming the ways that scientific and societal issues related to precipitation are addressed, in ways that would not be otherwise possible. These developments have taken place in parallel with the growth of an increasingly interconnected scientific environment. Scientists from different disciplines can easily interact with each other via information and materials they encounter online, and collaborate remotely without ever meeting each other in person. Likewise, these precipitation datasets are quickly and easily available via various data portals and are widely used. Within the framework of the NASA/JAXA Global Precipitation Measurement (GPM mission, these applications will become increasingly interconnected. We emphasize that precipitation observations by themselves provide an incomplete picture of the state of the atmosphere. For example, it is unlikely that a richer understanding of the global water cycle will be possible by standalone missions and algorithms, but must also involve some component of data, where model analyses of the physical state are constrained alongside multiple observations (e.g., precipitation, evaporation, radiation). The next section provides examples extracted from the many applications that use various high-resolution precipitation products. The final section summarizes the future system for global precipitation processing

Component analysis of errors in satellite-based precipitation estimates

Satellite-based precipitation estimates have great potential for a wide range of critical applications, but their error characteristics need to be examined and understood. In this study, six (6) high-resolution, satellite-based precipitation data sets are evaluated over the contiguous United States against a gauge-based product. An error decomposition scheme is devised to separate the errors into three independent components, hit bias, missed precipitation, and false precipitation, to better track the error sources associated with the satellite retrieval processes. Our analysis reveals the following. (1) The three components for each product are all substantial, with large spatial and temporal variations. (2) The amplitude of individual components sometimes is larger than that of the total errors. In such cases, the smaller total errors are resulting from the three components canceling one another. (3) All the products detected strong precipitation (\u3e40 mm/d) well, but with various biases. They tend to overestimate in summer and underestimate in winter, by as much as 50% in either season, and they all miss a significant amount of light precipitation (\u3c10 mm/d), up to 40%. (4) Hit bias and missed precipitation are the two leading error sources. In summer, positive hit bias, up to 50%, dominates the total errors for most products. (5) In winter, missed precipitation over mountainous regions and the northeast, presumably snowfall, poses a common challenge to all the data sets. On the basis of the findings, we recommend that future efforts focus on reducing hit bias, adding snowfall retrievals, and improving methods for combining gauge and satellite data. Strategies for future studies to establish better links between the errors in the end products and the upstream data sources are also proposed

An 11-Year Global Gridded Aerosol Optical Thickness Reanalysis (v1.0) for Atmospheric and Climate Sciences

While stand alone satellite and model aerosol products see wide utilization, there is a significant need in numerous atmospheric and climate applications for a fused product on a regular grid. Aerosol data assimilation is an operational reality at numerous centers, and like meteorological reanalyses, aerosol reanalyses will see significant use in the near future. Here we present a standardized 2003–2013 global 1 × 1 ◦ and 6-hourly modal aerosol optical thickness (AOT) reanalysis product. This data set can be applied to basic and applied Earth system science studies of significant aerosol events, aerosol impacts on numerical weather prediction, and electro-optical propagation and sensor performance, among other uses. This paper describes the science of how to develop and score an aerosol reanalysis product. This reanalysis utilizes a modified Navy Aerosol Analysis and Prediction System (NAAPS) at its core and assimilates quality controlled retrievals of AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Multi-angle Imaging SpectroRadiometer (MISR) on Terra. The aerosol source functions, including dust and smoke, were regionally tuned to obtain the best match between the model fine- and coarse-mode AOTs and the Aerosol Robotic Network (AERONET) AOTs. Other model processes, including deposition, were tuned to minimize the AOT difference between the model and satellite AOT. Aerosol wet deposition in the tropics is driven with satellite-retrieved precipitation, rather than the model field. The final reanalyzed fine- and coarse-mode AOT at 550 nm is shown to have good agreement with AERONET observations, with global mean root mean square error around 0.1 for both fine- and coarse-mode AOTs. This paper includes a discussion of issues particular to aerosol reanalyses that make them distinct from standard meteorological reanalyses, considerations for extending such a reanalysis outside of the NASA A-Train era, and examples of how the aerosol reanalysis can be applied or fused with other model or remote sensing products. Finally, the reanalysis is evaluated in comparison with other available studies of aerosol trends, and the implications of this comparison are discussed

Measurement Report: Long-Range Transport Patterns into the Tropical Northwest Pacific during the CAMP2Ex Aircraft Campaign: Chemical Composition, Size Distributions, and the Impact of Convection

The tropical Northwest Pacific (TNWP) is a receptor for pollution sources throughout Asia and is highly susceptible to climate change, making it imperative to understand long-range transport in this complex aerosol-meteorological environment. Measurements from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex; 24 August to 5 October 2019) and back trajectories from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) were used to examine transport into the TNWP from the Maritime Continent (MC), peninsular Southeast Asia (PSEA), East Asia (EA), and the West Pacific (WP). A mid-campaign monsoon shift on 20 September 2019 led to distinct transport patterns between the southwest monsoon (SWM; before 20 September) and monsoon transition (MT; after 20 September). During the SWM, long-range transport was a function of southwesterly winds and cyclones over the South China Sea. Low- (high-) altitude air generally came from MC (PSEA), implying distinct aerosol processing related to convection and perhaps wind shear. The MT saw transport from EA and WP, driven by Pacific northeasterly winds, continental anticyclones, and cyclones over the East China Sea. Composition of transported air differed by emission source and accumulated precipitation along trajectories (APT). MC air was characterized by biomass burning tracers while major components of EA air pointed to Asian outflow and secondary formation. Convective scavenging of PSEA air was evidenced by considerable vertical differences between aerosol species but not trace gases, as well as notably higher APT and smaller particles than other regions. Finally, we observed a possible wet scavenging mechanism acting on MC air aloft that was not strictly linked to precipitation. These results are important for understanding the transport and processing of air masses with further implications for modeling aerosol lifecycles and guiding international policymaking to public health and climate, particularly during the SWM and MT

The ROHP-PAZ Polarimetric Radio Occultation research dataset and its applications

Trabajo presentado al 7th International Workshop on Occultations for Probing Atmosphere and Climate y al 9th Workshop of the International Radio Occultation Working Group (OPAC-IROWG), celebrados del 8 al 14 de septiembre de 2022 en Leibnitz, Austria.After more than 4 years on orbit, the Radio Occultations and Heavy Precipitation aboard PAZ satellite (ROHP-PAZ) experiment has already demonstrated the ability of polarimetric radio occultations (PRO) to detect precipitation. In fact, PRO have shown potential not only in rain detection, but also in precipitation characterization and in sensing the associated vertical cloud structures. PAZ PRO Δϕ observable profiles were made available in 2020 trough the ICE servers (https://paz.ice.csic.es), (https://genesis.jpl.nasa.gov). and more recently through the JPL A new re-processing of the PRO observations is being carried out with the aim to make it public during the second half of 2022. In addition to a better treatment of the rainy observations, the new re-processed profiles will come with an extensive collocation dataset that will allow the users to address scientific studies much more easily. These will take into account the limb-sounding geometry of the observations, performing the collocations directly into the RO rays obtained through a ray-tracer. These collocations include observations like the 30-minute geostationary 10.8 µm brightness temperature, GPM IMERG surface precipitation, microwave brightness temperatures from the numerous overpasses by the satellites in the GPM constellation, radar reflectivities from the GPM core satellite and the NEXRAD ground based weather radars, among others. Furthermore, the collocation algorithms are designed so that more external observations can be easily included. In addition to the exact collocations as described above, external databases are also checked so that coincidences with Tropical Cyclones, Mesoscale Convective Systems and other relevant precipitating systems are identified nearby PAZ observations. In this presentation, we will show a brief overview of the re-processing of the ROHP-PAZ data, with emphasis in the differences between the Δϕ profiles obtained from UCAR’s CDAAC excess phases and from those obtained from JPL excess phases. After that, examples of the coincident datasets will be presented. Results will include statistics gathered from the differentiation of different precipitation regimes (e.g. stratiform vs convective), identification and validation of cloud top height determination, and comparison with other relevant parameters obtained from the collocated observations.The ROHP-PAZ project is part of the Grant RTI2018-099008-B-C22 funded by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” of the “European Union”. Part of the investigations are done under the EUMETSAT ROM SAF CDOP4. This work was partially supported by the program Unidad de Excelencia María de Maeztu CEX2020-001058-M. Part of this research has received funding from the postdoctoral fellowships program Beatriu de Pinós, funded by the Secretary of Universities and Research (Government of Catalonia) and by the Horizon 2020 program of research and innovation of the European Union under the Marie Sklodowska-Curie grant agreement No 801370.Peer reviewe

Cathepsin K Null Mice Show Reduced Adiposity during the Rapid Accumulation of Fat Stores

Growing evidences indicate that proteases are implicated in adipogenesis and in the onset of obesity. We previously reported that the cysteine protease cathepsin K (ctsk) is overexpressed in the white adipose tissue (WAT) of obese individuals. We herein characterized the WAT and the metabolic phenotype of ctsk deficient animals (ctsk−/−). When the growth rate of ctsk−/− was compared to that of the wild type animals (WT), we could establish a time window (5–8 weeks of age) within which ctsk−/−display significantly lower body weight and WAT size as compared to WT. Such a difference was not observable in older mice. Upon treatment with high fat diet (HFD) for 12 weeks ctsk−/− gained significantly less weight than WT and showed reduced brown adipose tissue, liver mass and a lower percentage of body fat. Plasma triglycerides, cholesterol and leptin were significantly lower in HFD-fed-ctsk−/− as compared to HFD-fed WT animals. Adipocyte lipolysis rates were increased in both young and HFD-fed-ctsk−/−, as compared to WT. Carnitine palmitoyl transferase-1 activity, was higher in mitochondria isolated from the WAT of HFD treated ctsk−/− as compared to WT. Together, these data indicate that ctsk ablation in mice results in reduced body fat content under conditions requiring a rapid accumulation of fat stores. This observation could be partly explained by an increased release and/or utilization of FFA and by an augmented ratio of lipolysis/lipogenesis. These results also demonstrate that under a HFD, ctsk deficiency confers a partial resistance to the development of dyslipidemia

Arsenic concentrations in seagrass around the Mediterranean coast and seasonal variations

Arsenic’s occurrence in the environment could be due to human activities as well as to natural sources. In this study, Posidonia oceanica and Cymodocea nodosa are collected in 84 sites around the Mediterranean basin. In addition, both seagrass are collected monthly, in two sites (Calvi in Corsica and Salammbô in Tunisia). Arsenic concentrations in C. nodosa present seasonal variations in relation with spring phytoplankton blooms. For both species arsenic concentration is higher in the vicinity of geological sources (mining), lagoon outlets and industrial activities. Moreover, Mediterranean islands (Balearic, Sardinia, Corsica, Malta, Crete and Cyprus) and the Southern basin coastline exhibit lower concentrations in Arsenic than the rest of the Mediterranean basin. The wide spread distribution of these two species would encourage their use in a global monitoring network devoted to Arsenic contamination.peer-reviewe

A Multi-center exercise on the sensitivity of PAZ GNSS Polarimetric RO for NWP modelling

Trabajo presentado al 7th International Workshop on Occultations for Probing Atmosphere and Climate y al 9th Workshop of the International Radio Occultation Working Group (OPAC-IROWG), celebrados del 8 al 14 de septiembre de 2022 en Leibnitz, Austria.A better understanding of the thermodynamics of heavy precipitation events is necessary towards improving weather and climate models and quantifying the impact of climate variability on precipitation. However, there are limited observations available to assess the model structure within heavy precipitation conditions. Recently, it has also been shown that the Radio Occultations Through Heavy Precipitation (ROHP) GNSS polarimetric radio occultation (GNSS PRO) observations are highly sensitive to hydrometeors above the freezing layer, which expands the potential uses of the GNSS PRO dataset for weather-related science and applications. An exercise is presented to analyze the sensitivity of PRO observations for NWP modeling applications. The ROHP experiment now provides over four years of coincident thermodynamic and precipitation information with high vertical resolution within regions with thick clouds. Murphy et al. (2019) simulated GNSS airborne polarimetric RO (GNSS PRO) events along an atmospheric river. These were modeled by the community WRF mesoscale model using two different microphysical parameterization schemes. The GNSS PRO observables simulated with the two schemes differed significantly, more than the actual GNSS PRO precision. The new exercise presented here reproduces this methodology for spaceborne data, using different global and regional NWP models, and it analyzes the results and divergences with the help of actual GNSS PRO data acquired aboard the PAZ satellite. The objectives of the activity are: (1) To compare simulated GNSS PRO observables, generated with models from different centers and different microphysics schemes, against actual PAZ GNSS PRO observables. Can the models reproduce the main features of the actual data? (2) To assess whether different models/schemes result in different GNSS PRO observables, and whether these differences are larger than the measurement uncertainty. This effort provides insight on future methods to assimilate the PRO profile alongside other conventional (non-polarimetric) RO data. (3) To examine the utility of PAZ GNSS PRO observations for model validation and diagnosis. The exercise includes comparisons with ECWMF reanalysis ERA-5 model, the operational NWP at the Japan Meteorological Agency, and a near-real-time implementation of the WRF regional model over the northeastern Pacific produced at the Center for Western Weather and Water Extremes (CW3E) called West WRF, among others.The ROHP-PAZ project is part of the Grant RTI2018-099008-B-C22 funded by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” of the “European Union”. Part of the investigations are done under the EUMETSAT ROM SAF CDOP4. This work was partially supported by the program Unidad de Excelencia María de Maeztu CEX2020-001058-M. Part of this research has received funding from the postdoctoral fellowships program Beatriu de Pinós, funded by the Secretary of Universities and Research (Government of Catalonia) and by the Horizon 2020 program of research and innovation of the European Union under the Marie Sklodowska-Curie grant agreement No 801370.Peer reviewe