Program Special Sensor Microwave Imager Research Articles

Improvement of the Rain/No-Rain Classification Method for Microwave Radiometers Over the Tibetan Plateau

This letter identifies causes of the deterioration of the Global Satellite Mapping of Precipitation rain detection over the Tibetan Plateau during the summer monsoon season. Using the rain/no-rain classification (RNC) method over the Plateau, observed brightness temperature (Tb) at 21 (23) GHz from the Tropical Rainfall Measuring Mission Microwave Imager (the Defense Meteorological Satellite Program Special Sensor Microwave Imager) [Tb(21 V) and Tb(23 V)], and surface emissivity ( $\varepsilon )$ was substituted for surface temperatures (Ts) to exclude areas of low Ts as snow cover because it is difficult to distinguish between the scattering signals of precipitation and snow cover. A case study demonstrates that rain systems are excluded because Ts is often below the threshold for snow cover due to the use of an inadequate value for $\varepsilon $ (constant value throughout the year), even though a rain scattering signal at high-frequency channels under no snow cover is evident. After $\varepsilon $ is replaced with values from a monthly mean satellite observation-based land surface emissivity database, rainfall detection is improved. In addition, it is suggested that a database of RNCs should also consider diurnal variations in Tb(21 V) and Tb(23 V) due to large diurnal differences over the Plateau.

Special Sensor Microwave Imager (SSM/I) Intersensor Calibration Using a Simultaneous Conical Overpass Technique

AbstractA new intersensor calibration scheme is developed for the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) to correct its scan-angle-dependent bias, the radar calibration beacon interference on the F-15 satellite, and other intersensor biases. The intersensor bias is characterized by the simultaneous overpass measurements with the F-13 SSM/I as a reference. This sensor data record (SDR) intersensor calibration procedure is routinely running at the National Oceanic and Atmospheric Administration and is now used for reprocessing all SSM/I environmental data records (EDR), including total precipitable water (TPW) and surface precipitation. Results show that this scheme improves the consistency of the monthly SDR’s time series from different SSM/I sensors. Relative to the matched rain products from the Tropical Rainfall Measuring Mission, the bias of SSM/I monthly precipitation is reduced by 12% after intersensor calibration. TPW biases between sensors are reduced by 75% over the global ocean and 20% over the tropical ocean, respectively. The intersensor calibration reduces biases by 20.6%, 15.7%, and 6.5% for oceanic, land, and global precipitation, respectively. The TPW climate trend is 1.59% decade−1 (or 0.34 mm decade−1) for the global ocean and 1.39% decade−1 (or 0.63 mm decade−1) for the tropical ocean, indicating related trends decrease of 38% and 54%, respectively, from the uncalibrated SDRs. Results demonstrate the large impacts of this calibration on the TPW climate trend.

Monitoring Dry, Wet, and No-Snow Conditions From Microwave Satellite Observations

Possible climate warming in northern latitudes will affect stream flow in watersheds dominated by snowmelt. It is important to detect early snowmelt conditions for applications to flood forecasting and monitoring fresh water stored in snow cover. This letter presents a study to demonstrate the potential of combined active and passive microwave spaceborne observations for monitoring global to regional snow cover on a daily basis. The proposed approach uses the temporal gradient of two parameters: 1) the Ku-band QuikSCAT scatterometer backscattering coefficient variation for snow wetness detection and 2) a dual-frequency emissivity index (Δϵt) derived from the Defense Meteorological Satellite Program Special Sensor Microwave Imager brightness temperatures for snow line detection. This approach takes into account the land cover derived from satellite data on a pixel-by-pixel basis. The evolution of the backscatter and Δϵt signatures throughout the winter-spring seasonal snow cycle is compared with in situ snow and air temperature measurements and with the snow-cover maps derived from high-resolution satellite data over Eastern Canada. The results show the high potential of this approach for historical analysis, as well as for day-to-day prospective investigation (forecasting).

An Extended and Improved Special Sensor Microwave Imager (SSM/I) Period of Record

Abstract The National Oceanic and Atmospheric Administration National Climatic Data Center has served as the archive of the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) data from the F-8, F-10, F-11, F-13, F-14, and F-15 platforms covering the period from July 1987 to the present. Passive microwave satellite measurements from SSM/I have been used to generate climate products in support of national and international programs. The SSM/I temperature data record (TDR) and sensor data record (SDR) datasets have been reprocessed and stored as network Common Data Form (netCDF) 3-hourly files. In addition to reformatting the data, a normalized anomaly (z score) for each footprint temperature value was calculated by subtracting each radiance value with the corresponding monthly 1° grid climatological mean and dividing it by the associated climatological standard deviation. Threshold checks were also used to detect radiance, temporal, and geolocation values that were outside the expected ranges. The application of z scores and threshold parameters in the form of embedded quality flags has improved the fidelity of the SSM/I TDR/SDR period of record for climatological applications. This effort has helped to preserve and increase the data maturity level of the longest satellite passive microwave period of record while completing a key first step before developing a homogenized and intercalibrated SSM/I climate data record in the near future.

Multisensor historical climatology of satellite‐derived global land surface moisture

A historical climatology of continuous satellite‐derived global land surface soil moisture is being developed. The data consist of surface soil moisture retrievals derived from all available historical and active satellite microwave sensors, including Nimbus‐7 Scanning Multichannel Microwave Radiometer, Defense Meteorological Satellites Program Special Sensor Microwave Imager, Tropical Rainfall Measuring Mission Microwave Imager, and Aqua Advanced Microwave Scanning Radiometer for EOS, and span the period from November 1978 through the end of 2007. This new data set is a global product and is consistent in its retrieval approach for the entire period of data record. The moisture retrievals are made with a radiative transfer‐based land parameter retrieval model. The various sensors have different technical specifications, including primary wavelength, spatial resolution, and temporal frequency of coverage. These sensor specifications and their effect on the data retrievals are discussed. The model is described in detail, and the quality of the data with respect to the different sensors is discussed as well. Examples of the different sensor retrievals illustrating global patterns are presented. Additional validation studies were performed with large‐scale observational soil moisture data sets and are also presented. The data will be made available for use by the general science community.

Analysis of Ground-Measured and Passive-Microwave-Derived Snow Depth Variations in Midwinter across the Northern Great Plains

Abstract Accurate estimation of snow mass is important for the characterization of the hydrological cycle at different space and time scales. For effective water resources management, accurate estimation of snow storage is needed. Conventionally, snow depth is measured at a point, and in order to monitor snow depth in a temporally and spatially comprehensive manner, optimum interpolation of the points is undertaken. Yet the spatial representation of point measurements at a basin or on a larger distance scale is uncertain. Spaceborne scanning sensors, which cover a wide swath and can provide rapid repeat global coverage, are ideally suited to augment the global snow information. Satellite-borne passive microwave sensors have been used to derive snow depth (SD) with some success. The uncertainties in point SD and areal SD of natural snowpacks need to be understood if comparisons are to be made between a point SD measurement and satellite SD. In this paper three issues are addressed relating satellite derivation of SD and ground measurements of SD in the northern Great Plains of the United States from 1988 to 1997. First, it is shown that in comparing samples of ground-measured point SD data with satellite-derived 25 × 25 km2 pixels of SD from the Defense Meteorological Satellite Program Special Sensor Microwave Imager, there are significant differences in yearly SD values even though the accumulated datasets showed similarities. Second, from variogram analysis, the spatial variability of SD from each dataset was comparable. Third, for a sampling grid cell domain of 1° × 1° in the study terrain, 10 distributed snow depth measurements per cell are required to produce a sampling error of 5 cm or better. This study has important implications for validating SD derivations from satellite microwave observations.

Variational Retrievals Using SSM/I and SSM/T-2 Brightness Temperatures in Clear and Cloudy Situations

Abstract The direct assimilation of brightness temperatures (Tb’s) from the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Water Vapor Profiler (SSM/T-2) in a one-dimensional variational (1D-Var) assimilation system is studied over the oceans. The control variables of the 1D-Var are the natural logarithm of specific humidity (lnq), near-surface wind speed (SWS), and cloud liquid water (CLW) path. Synthetic Tb’s, with and without noise added, were simulated and used to estimate the strength of the 1D-Var assimilation (weight given to the observations) and the information content of the Tb. In clear skies, it is shown that except for very dry profiles (TPW < 5–6 kg m−2) SSM/I Tb’s are superior to the SSM/T-2 Tb’s for the determination of total precipitable water (TPW). In the presence of clouds, the SSM/T-2 TPW retrievals are underestimated and the underestimation increases with CLW. Cloudy profiles should be filtered out. It is also shown that ...

Results from an ECMWF‐SSM/I forced mixed layer model of the Southern Ocean

A bulk mixed layer model of the entire Southern Ocean forced by European Centre for Medium‐Range Weather Forecasts (ECMWF) near‐surface air temperatures and winds is presented. In addition, the Southern Ocean sea ice concentration determined from daily Defense Meteorological Satellite Program special sensor microwave imager (SSM/I) observations obviates the need for a dynamic‐thermodynamic sea ice model. The oceanic mixed layer serves as the interface between the deep ocean and the atmosphere or sea ice. Measurements have shown that in the Southern Ocean entrainment of relatively warm deep water into the mixed layer has a significant impact on the oceanic stability and is the primary reason for the thinness of its sea ice cover. The use of satellite‐derived ice concentrations reduces greatly the required accuracy in air temperature and wind needed by the model compared to the accuracy needed by coupled sea ice ocean models because these data are only needed here in the calculation of salt and heat fluxes and not to derive ice cover or ice dynamics. The results show that the seasonal cycle and the spatial distribution of mixed layer temperature, salinity, and depth can be reproduced and are in good agreement with in situ observations. Limitations in accuracy result predominantly still from uncertainties in air temperature, cloud cover, and wind speed during the summer months for areas with no or little ice coverage, ice concentration, and snow depth during the winter months as well as ocean currents.

NASA team algorithm for sea ice concentration retrieval from Defense Meteorological Satellite Program special sensor microwave imager: Comparison with Landsat satellite imagery

Validation of the NASA team algorithm for the determination of sea ice concentrations from the Defense Meteorological Satellite Program special sensor microwave imager (SSM/I) is described. A total of 28 cloud‐free Landsat scenes were selected in order to permit validation of the passive microwave ice concentration algorithm for a range of ice concentrations and ice types. The sensitivity of the NASA team algorithm to the selection of locally and seasonally adjusted algorithm parameters is discussed in detail. Mean absolute differences between SSM/I and Landsat ice concentrations are within 1% during fall using local and global tie points. Standard deviations of the difference are ±3.1% and ±6.2% respectively. The overall accuracy of the NASA team algorithm is lower in spring than in fall. In areas with greater amounts of nilas and young ice, we found that the NASA team algorithm underestimates ice concentrations by as much as 9%. The Landsat and SSM/I ice concentrations have a correlation of 0.968 for all spring and fall case studies with a standard deviation of ±6.6% using global tie points and a correlation of 0.982 with a standard deviation of ±4.5% using local tie points. The NASA team algorithm tends to underestimate ice concentration in areas of close pack ice and to overestimate ice concentrations in areas of open pack ice. In summer, mean differences between SSM/I and Landsat ice concentrations are 3.8% for local tie points and 11.0% for global tie points for Arctic areas and 7.2% for local tie points and 11.7% for global tie points for Antarctic areas. These large differences are attributable to surface melt during summer and comparison problems arising from a time lag of up to 8 hours between the DMSP and Landsat satellites. It appears that seasonally and regionally adjusted tie points (local tie points) will improve the overall performance of the NASA team algorithm. Our work suggests that the standard deviation between SSM/I and Landsat ice concentrations decreases from ±7% to ±5% with local tie points compared to global ones for spring and fall.

Aircraft active and passive microwave validation of sea ice concentration from the Defense Meteorological Satellite Program special sensor microwave imager

During March 1988 a series of coordinated special sensor microwave imager (SSM/I) underflights were carried out with NASA and Navy aircraft over portions of the Bering, Beaufort, and Chukchi seas as part of the NASA Defense Meteorological Satellite Program SSM/I Sea Ice Validation Program. The two Navy research aircraft, a Naval Research Laboratory P‐3 with the NOARL Ka band radiometric mapping system operating at 33.6 GHz and a Naval Air Development Center (NADC) P‐3 with the NADC‐Environmental Research Institute of Michigan (ERIM) C band synthetic aperture radar (SAR), provided wide‐swath, high‐resolution microwave imagery for direct comparison with sea ice concentrations calculated from SSM/I radiances using the NASA sea ice algorithm. Coincident measurements made with the Jet Propulsion Laboratory (JPL) C band SAR and the Goddard Space Flight Center (GSFC) aircraft multifrequency microwave radiometers (AMMR) on the NASA DC‐8 airborne laboratory provided additional verification of the algorithm. NASA DC‐8 AMMR data from Bering Sea ice edge crossings were used to verify that the ice edge location, defined as the position of the initial ice bands encountered by the aircraft, corresponds to an SSM/I ice concentration of 15%. Direct comparison of SSM/I and aircraft ice concentrations for regions having at least 80% aircraft coverage reveals that the SSM/I total ice concentration is lower on average by 2.4%±2.4%. For multiyear ice, NASA and Navy flights across the Beaufort and Chukchi seas show that the SSM/I algorithm correctly maps the large‐scale distribution of multiyear ice: the zone of first‐year ice off the Alaskan coast, the large areas of mixed first‐year and multiyear ice, and the region of predominantly multiyear ice north of the Canadian archipelago. Quantitative comparisons show that the SSM/I algorithm overestimates multiyear ice concentration by 12%±11% on average in the Chukchi and Beaufort seas. Excluding data for a day which gave anomalously large positive biases, the multiyear ice concentration difference reduces to 5%±4%, also indicating a positive SSM/I bias. Anomalously low SSM/I concentrations were found in the coastal zone north of Ellesmere Island. Differences between multiyear ice concentrations estimated from the JPL C band SAR imagery and from the GSFC AMMR radiances using an SSM/I type algorithm show that the AMMR concentrations are smaller on average by 6%±14%. Sea ice conditions are described, and possible causes of the observed differences are discussed.

Multifrequency polarimetric synthetic aperture radar observations of sea ice

The first known fully polarimetric airborne synthetic aperture radar (SAR) data set of sea ice is introduced. Images were acquired in the Beaufort, Bering and Chukchi seas in March 1988, during a campaign for validation of Defense Meteorological Satellite Program Special Sensor Microwave Imager radiometer ice products. Statistics of the magnitude, phase and polarization of complex backscattered signals recorded by the Jet Propulsion Laboratory three‐frequency SAR are examined in detail for various scenes with different ice characteristics. The full Stokes matrix information generated from C, L, and P band data characterize the scattering behavior of different ice types. Polarization ratios and phase differences between linear copolarized returns are used for discrimination between particular image features and mechanisms are suggested for the observed polarimetric characteristics. Results indicate that combinations of frequency and polarization enhance current capability to distinguish ice of different properties using single frequency, fixed polarization micro‐wave radar. A specific example is the polarimetric identification of new ice formation which may not be easily distinguished in ERS‐1 5.3GHz, VV polarization SAR data. Such findings are consistent with theoretical model simulations of scattering characteristics of sea ice. Overall, these preliminary results demonstrate that radar polarimetry will likely add a new dimension to our current capability for extracting geophysically important ice variables using radar remote sensing methods.