Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

Measurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA\u27s SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation\u27s (ISRO\u27s) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)\u27s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broad-based research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results

Satellite Remote Sensing of Tropical Cyclones

This chapter provides a review on satellite remote sensing of tropical cyclones (TCs). Applications of satellite remote sensing from geostationary (GEO) and low earth orbital (LEO) platforms, especially from passive microwave (PMW) sensors, are focused on TC detection, structure, and intensity analysis as well as precipitation patterns. The impacts of satellite remote sensing on TC forecasts are discussed with respect to helping reduce the TC\u27s track and intensity forecast errors. Finally, the multi‐satellite‐sensor data fusion technique is explained as the best way to automatically monitor and track the global TC\u27s position, structure, and intensity

Assimilating MODIS and AMSR-E Snow Observations in a Snow Evolution Model

In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent

Assessment of the synoptic variability of the Antarctic marginal ice zone with in Situ observations

Knowledge of sea ice variability, which contributes to the detection of climate change trends, stems primarily from remote sensing information. However, sea ice in the Southern Ocean is characterised by large variability that remains unresolved and limits our confidence on the remotely sensed products. Although one of the biggest seasonal changes on Earth is the annual advance and retreat of the Antarctic sea ice cover, relatively little attention has been given to the processes by which the marginal ice zone (MIZ) edge forms and responds to synoptic events. This study aimed to assess the seasonal sea ice extent (SIE) of the MIZ by comparing sea ice observations estimated from aboard ship to high resolution passive microwave (PM) satellite imagery when transecting the MIZ. To achieve this, sea ice concentration (SIC) was derived from two AMSR (Advanced Microwave Scanning Radiometer ) products; the ARTIST (Arctic Radiation and Turbulence Interaction STudy) Sea Ice (ASI-AMSR ) and the bootstrap (BST-AMSR ). Theice concentration estimated from these PM satellite products was assessed against SIC observations collected from the S.A. Agulhas II (using the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol). This assessment took place over summer and winter for the years 2016 and 2017. After evaluating how well these PM-SIC estimates compared against the ASPeCt SIC observations, we found that there was good correlation over summer MIZ conditions, while over winter MIZ conditions the correlation was relatively poor. This highlighted winter limitations inherent in PM SIC estimates. Therefore, from these comparison results, an analysis of the seasonal SIE was accomplished while being aware of the winter limitations linked to the PM products. We inferred that the MIZ acts as an indicator for what the evolution of winter SIE might look like over the following months. In addition to winter limitations associated with PM-SIC retrievals, the ASPeCt SIC estimates, based on human interpretation of the sea ice conditions, was limited because of subjective bias. This resulted in the development of an algorithm to automatically acquire SIC from image stills and videos. This method can be used to obtain quantitative seaice data from vessels of opportunity without the need to have trained personnel on-board. In summary, this study assesses seasonal MIZ SIE within the Atlantic sector after highlighting the limitations associated with various SIC-retrieval methods

FluxSat: measuring the ocean-atmosphere turbulent exchange of heat and moisture from space

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Gentemann, C. L., Clayson, C. A., Brown, S., Lee, T., Parfitt, R., Farrar, J. T., Bourassa, M., Minnett, P. J., Seo, H., Gille, S. T., & Zlotnicki, V. FluxSat: measuring the ocean-atmosphere turbulent exchange of heat and moisture from space. Remote Sensing, 12(11), (2020): 1796, doi:10.3390/rs12111796.Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean–atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air–sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean–atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean–atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations.C.L.G. was funded by NASA grant 80NSSC18K0837. C.A.C. was funded by NASA grants 80NSSC18K0778 and 80NSSC20K0662. J.T.F. was funded by NASA grants NNX17AH54G, NNX16AH76G, and 80NSSC19K1256. S.T.G. was funded by the National Science Foundation grant PLR-1425989 and by the NASA Ocean Vector Winds Science Team grant 80NSSC19K0059. M.B. was funded in part by the Ocean Observing and Monitoring Division, Climate Program Office (FundRef number 100007298), National Oceanic and Atmospheric Administration, U.S. Department of Commerce, and by the NASA Ocean Vector Winds Science Team grant through NASA/JPL. H.S. was funded by National Oceanic and Atmospheric Administration (NOAA) grant NA19OAR4310376 and the Andrew W. Mellon Foundation Endowed Fund for Innovative Research at Woods Hole Oceanographic Institution

Radiometric correction of scatterometric wind measurements

Use of a spaceborne scatterometer to determine the ocean-surface wind vector requires accurate measurement of radar backscatter from ocean. Such measurements are hindered by the effect of attenuation in the precipitating regions over sea. The attenuation can be estimated reasonably well with the knowledge of brightness temperatures observed by a microwave radiometer. The NASA SeaWinds scatterometer is to be flown on the Japanese ADEOS2. The AMSR multi-frequency radiometer on ADEOS2 will be used to correct errors due to attenuation in the SeaWinds scatterometer measurements. Here we investigate the errors in the attenuation corrections. Errors would be quite small if the radiometer and scatterometer footprints were identical and filled with uniform rain. However, the footprints are not identical, and because of their size one cannot expect uniform rain across each cell. Simulations were performed with the SeaWinds scatterometer (13.4 GHz) and AMSR (18.7 GHz) footprints with gradients of attenuation. The study shows that the resulting wind speed errors after correction (using the radiometer) are small for most cases. However, variations in the degree of overlap between the radiometer and scatterometer footprints affect the accuracy of the wind speed measurements

Passive millimeter-wave retrieval of global precipitation utilizing satellites and a numerical weather prediction model

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 229-234).This thesis develops and validates the MM5/TBSCAT/F([lambda]) model, composed of a mesoscale numerical weather prediction (NWP) model (MM5), a two-stream radiative transfer model (TBSCAT), and electromagnetic models for icy hydrometeors (F([lambda])), to be used as a global precipitation ground-truth for evaluating alternative millimeter-wave satellite designs and for developing methods for millimeter-wave precipitation retrieval and assimilation. The model's predicted millimeter-wave atmospheric radiances were found to statistically agree with those observed by satellite instruments [Advanced Microwave Sounding Unit-A/B (AMSU-A/B)] on the United States National Ocean and Atmospheric Administration NOAA-15, -16, and -17 satellites over 122 global representative storms. Whereas such radiance agreement was found to be sensitive to assumptions in MM5 and the radiative transfer model, precipitation retrieval accuracies predicted using the MM5/TBSCAT/F([lambda]) model were found to be robust to the assumptions.(cont.) Appropriate specifications for geostationary microwave sounders and their precipitation retrieval accuracies were studied. It was found that a 1.2-m micro-scanned filled-aperture antenna operating at 118/166/183/380/425 GHz, which is relatively inexpensive, simple to build, technologically mature, and readily installed on a geostationary satellite, could provide useful observation of important global precipitation with ~20-km resolution every 15 minutes. AMSU global precipitation retrieval algorithms for retrieving surface precipitation rate, peak vertical wind, and water-paths for rainwater, snow, graupel, cloud water, cloud ice, and the sum of rainwater, snow, and graupel, over non-icy surfaces were developed separately using a statistical ensemble of global precipitation predicted by the MM5/TBSCAT/F([lambda]) model. Different algorithms were used for land and sea, where principal component analysis was used to attenuate unwanted noises, such as surface effects and angle dependence. The algorithms were found to perform reasonably well for all types of precipitation as evaluated against MM5 ground-truth. The algorithms also work over land with snow and sea ice, but with a strong risk of false detections. AMSU surface precipitation rates retrieved using the algorithm developed in this thesis reasonably agree with those retrieved for the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) aboard the Aqua satellite over both land and sea.(cont.) Surface precipitation rates retrieved using the Advanced Microwave Sounding Unit (AMSU) aboard NOAA-15 and -16 satellites were further compared with four similar products derived from other systems that also observed the United States Great Plains (USGP) during the summer of 2004. These systems include AMSR-E aboard the Aqua satellite, the Special Sensor Microwave/Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites, the passive Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) aboard the TRMM satellite, and a surface precipitation rate product (NOWRAD), produced and marketed by Weather Services International Corporation (WSI) using observations from the Weather Surveillance Radar-1988 Doppler (WSR-88D) systems of the Next-Generation Weather Radar (NEXRAD) program. The results show the reasonable agreement among these surface precipitation rate products where the difference is mostly in the retrieval resolution, which depends on instruments' characteristics. A technique for assimilating precipitation information from observed millimeter-wave radiances to MM5 model was proposed. Preliminary study shows that wind and other correction techniques could help align observations at different times so that information from observed radiances is used at appropriate locations.by Chinnawat Surussavadee.Ph.D

Master of Science

thesisGlobal analysis fields, infrared and passive microwave satellite observations, lightning data, and airborne radar reflectivity and dual-Doppler wind analyses show the evolution of environmental conditions, precipitation characteristics, and kinematic structure before, during, and after the rapid intensification (RI) of Hurricane Earl (2010). The relationship between the RI and environmental conditions, intense inner-core convection, inner-core precipitation coverage, core cold-cloud precipitation symmetry, and the radial distribution of convection is examined. The onset of RI occurs despite moderate vertical wind shear. An episode of intense convection occurs before the RI onset, but an examination of the mesoscale and convective-scale kinematic processes during this convective ‘burst' suggests that the strength of convection alone did not cause the onset of RI. Instead, the dual-Doppler, lightning, and microwave data suggest that the precipitation characteristic that ultimately led to the onset of RI was an increasing trend in cold-cloud precipitation symmetry following the migration of inner-core convection into the northeastern and northern quadrants of the storm within a few hours before RI onset. The evolution of precipitation during the RI suggests that the most important inner-core precipitation characteristics supporting RI are the cold-cloud precipitation symmetry and the predominance of strong convective updrafts within (instead of outside of) the radius of maximum wind (RMW). The wind and precipitation data from Earl indicate that the RMW at multiple levels must be examined. When the RMW is substantially slanted, only considering the low-level RMW can lead to the false conclusion that the strongest convection is located outside of the RMW

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne