The Split Window Microwave Radiometer (SWMR) for hurricane wind speed measurement from space

The monitoring of hurricanes demands considerable resources each year by the National Oceanic and Atmospheric Administration. Even with the extensive use of satellite and airborne probing of those storms, there is still much uncertainty involved in predicting landfall for timely evacuation of people subject to the threat. The concept of the Split Window Microwave Radiometer (SWMR) is to add an additional capability of remotely measuring surface winds to hopefully improve prediction capabilities or at least define the severity of the storm while it is far from land. Some of the present science and observational needs are addressed in this report as are remote sensing limitations which impact the design of a minimal system which can be launched into low earth orbit by a low cost launch system. This study has concluded that wind speed and rain rate maps of hurricanes can be generated with an X-Band radiometer system with an antenna whose aperture is 2 m on a side

NASA sea ice and snow validation plan for the Defense Meteorological Satellite Program special sensor microwave/imager

This document addresses the task of developing and executing a plan for validating the algorithm used for initial processing of sea ice data from the Special Sensor Microwave/Imager (SSMI). The document outlines a plan for monitoring the performance of the SSMI, for validating the derived sea ice parameters, and for providing quality data products before distribution to the research community. Because of recent advances in the application of passive microwave remote sensing to snow cover on land, the validation of snow algorithms is also addressed

Measuring Geophysical Parameters of the Greenland Ice Sheet using Airborne Radar Altimetry

This paper presents radar-altimeter scattering models for each of the diagenetic zones of the Greenland ice sheet. AAFE radar- altimeter waveforms obtained during the 1991 and 1993 NASA multi-sensor airborne altimetry experiments over Greenland reveal that the Ku-band return pulse changes significantly with the different diagenetic zones. These changes are due to varying amounts of surface and volume scattering in the return waveform. In the ablation and soaked zones, where surface scattering dominates the AAFE return, geophysical parameters such as rms surface height and rms surface slope are obtained by fitting the waveforms to a surface-scattering model. Waveforms from the percolation zone show that the sub-surface ice features have a much more significant effect on the return pulse than the surrounding snowpack. Model percolation waveforms, created using a combined surface- and volume-scattering model and an ice-feature distribution obtained during the 1993 field season, agree well with actual AAFE waveforms taken in the same time period. Using a combined surface- and volume-scattering model for the dry-snow-zone return waveforms, the rms surface height and slope and the attenuation coefficient of the snowpack are obtained. These scattering models not only allow geophysical parameters of the ice sheet to be measured but also help in the understanding of satellite radar-altimeter data

Comparison of Retracking Algorithms Using Airborne Radar and Laser Altimeter Measurements of the Greenland Ice Sheet

This paper compares four continental ice sheet radar altimeter retracking algorithms using airborne radar and laser altimeter data taken over the Greenland ice sheet in 1991. The refurbished Advanced Application Flight Experiment (AAFE) airborne radar altimeter has a large range window and stores the entire return waveform during flight. Once the return waveforms are retracked, or post-processed to obtain the most accurate altitude measurement possible, they are compared with the high-precision Airborne Oceanographic Lidar (AOL) altimeter measurements. The AAFE waveforms show evidence of varying degrees of both surface and volume scattering from different regions of the Greenland ice sheet. The AOL laser altimeter, however, obtains a return only from the surface of the ice sheet. Retracking altimeter waveforms with a surface scattering model results in a good correlation with the laser measurements in the wet and dry-snow zones, but in the percolation region of the ice sheet, the deviation between the two data sets is large due to the effects of subsurface and volume scattering. The Martin et al model results in a lower bias than the surface scattering model, but still shows an increase in the noise level in the percolation zone. Using an Offset Center of Gravity algorithm to retrack altimeter waveforms results in measurements that are only slightly affected by subsurface and volume scattering and, despite a higher bias, this algorithm works well in all regions of the ice sheet. A cubic spline provides retracked altitudes that agree with AOL measurements over all regions of Greenland. This method is not sensitive to changes in the scattering mechanisms of the ice sheet and it has the lowest noise level and bias of all the retracking methods presented

Sea Surface Salinity: The Next Remote Sensing Challenge

A brief history of salinity remote sensing is presented. The role of sea surface salinity (SSS) in the far north Atlantic and the influence of salinity variations on upper ocean dynamics in the tropics are described. An assessment of the present state of the technology of the SSS satellite remote sensing is given

Validation of ERS-1 environmental data products

Evaluation of the launch-version algorithms used by the European Space Agency (ESA) to derive wind field and ocean wave estimates from measurements of sensors aboard the European Remote Sensing satellite, ERS-1, has been accomplished through comparison of the derived parameters with coincident measurements made by 24 open ocean buoys maintained by the National Oceanic and Atmospheric Administration). During the period from November 1, 1991 through February 28, 1992, data bases with 577 and 485 pairs of coincident sensor/buoy wind and wave measurements were collected for the Active Microwave Instrument (AMI) and Radar Altimeter (RA) respectively. Based on these data, algorithm retrieval accuracy is estimated to be plus or minus 4 m/s for AMI wind speed, plus or minus 3 m/s for RA wind speed and plus or minus 0.6 m for RA wave height. After removing 180 degree ambiguity errors, the AMI wind direction retrieval accuracy was estimated at plus or minus 28 degrees. All of the ERS-1 wind and wave retrievals are relatively unbiased. These results should be viewed as interim since improved algorithms are under development. As final versions are implemented, additional assessments should be conducted to complete the validation

NASA Tropical Rainfall Measurement Mission (TRMM): Effects of tropical rainfall on upper ocean dynamics, air-sea coupling and hydrologic cycle

This was a Tropical Rainfall Measurement Mission (TRMM) modeling, analysis and applications research project. Our broad scientific goals addressed three of the seven TRMM Priority Science Questions, specifically: What is the monthly average rainfall over the tropical ocean areas of about 10(exp 5) sq km, and how does this rain and its variability affect the structure and circulation of the tropical oceans? What is the relationship between precipitation and changes in the boundary conditions at the Earth's surface (e.g., sea surface temperature, soil properties, vegetation)? How can improved documentation of rainfall improve understanding of the hydrological cycle in the tropics

Retrieval of atmospheric attenuation using combined ground-based and airborne 95-GHz cloud radar measurements

Includes bibliographical references (page 1353).Cloud measurements at millimeter-wave frequencies are affected by attenuation due to atmospheric gases, clouds, and precipitation. Estimation of the true equivalent radar reflectivity, Ze, is complicated because extinction mechanisms are not well characterized at these short wavelengths. This paper discusses cloud radar calibration and intercomparison of airborne and ground-based radar measurements and presents a unique algorithm for attenuation retrieval. This algorithm is based on dual 95-GHz radar measurements of the same cloud and precipitation volumes collected from opposing viewing angles. True radar reflectivity is retrieved by combining upward-looking and downward-looking radar profiles. This method reduces the uncertainty in radar reflectivity and attenuation estimates, since it does not require a priori knowledge of hydrometeors' microphysical properties. Results from this technique are compared with results retrieved from the Hitschfeld and Bordan algorithm, which uses single-radar measurements with path-integrated attenuation as a constraint. Further analysis is planned to employ this dual-radar algorithm in order to refine single-radar attenuation retrieval techniques, which will be used by operational sensors such as the CloudSat radar