The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
C C Wackerman - One of the best experts on this subject based on the ideXlab platform.
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aircraft active and passive microwave validation of sea ice concentration from the defense Meteorological Satellite program special sensor microwave imager
Journal of Geophysical Research, 1991Co-Authors: Donald J Cavalieri, J P Crawford, M R Drinkwater, Duane T Eppler, L D Farmer, R R Jentz, C C WackermanAbstract: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.
Axel Schweiger - One of the best experts on this subject based on the ideXlab platform.
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nasa team algorithm for sea ice concentration retrieval from defense Meteorological Satellite program special sensor microwave imager comparison with landsat Satellite imagery
Journal of Geophysical Research, 1991Co-Authors: Konrad Steffen, Axel SchweigerAbstract: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.
Norman C Grody - One of the best experts on this subject based on the ideXlab platform.
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global precipitation estimations using defense Meteorological Satellite program f10 and f11 special sensor microwave imager data
Journal of Geophysical Research, 1994Co-Authors: Fuzhong Weng, Ralph Ferraro, Norman C GrodyAbstract:F10 and F11 Satellites from the Defense Meteorological Satellite Program currently observe the earth-atmosphere system four times a day, at 0530, 1030, 1730, and 2230 local solar time. This study uses the special sensor microwave imager data from both Satellites to retrieve precipitation over land and ocean and presents some preliminary results of the spatial and temporal variations of rainfall during a month. Diurnal variations of precipitation over the globe are analyzed for August 1993 using measurements from both Satellites. Over most oceans, precipitation displays the well-known morning maximum. However, over land, precipitation is highly variable for the study period. Error analyses indicate that over ocean the monthly precipitation estimates from the F11 Satellite alone may result in 20–25% errors due to insufficient temporal sampling. This error increases over some land regions to as much as 50–70%. This study also analyses the difference between the monthly rainfall estimates using a sample-averaging method and that using a lognormal probability density function. It is found that an overall difference over the globe is small with less than 5%.
Qiang Xing - One of the best experts on this subject based on the ideXlab platform.
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Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite
Atmosphere, 2019Co-Authors: Weiwei Zhu, Nana Yan, Linjiang Wang, Wenjun Liu, Qiang XingAbstract:Sunshine duration is an important indicator of the amount of solar radiation received in a region and an important input parameter for the study of atmospheric energy balance, climate change, ecosystem evolution, and social sustainability. Currently, extrapolation and interpolation of data from Meteorological stations are the most common methods used to calculate sunshine duration on a regional scale. However, it is difficult to obtain high precision sunshine duration in areas lacking ground observation or where sunshine duration is highly heterogeneous on the ground. In this paper, a new method is proposed to estimate sunshine duration with hourly total cloud amount (CTA) data from sunrise to sunset derived from the Fengyun-2G geostationary Meteorological Satellite (FY-2G). This method constructs a new index known as daytime mean total cloud coverage amount and provides quadratic equations relating daytime mean total cloud coverage amount to relative sunshine duration in different seasons. The method was validated with ground observation data for 2016 from 18 Meteorological stations in the Three-River Headwaters Region of Qinghai Province, China. For individual stations, the coefficient of determination (R2) between estimated and measured sunshine was at least 0.894, the RMSE (root mean square error) was 0.977 h/day or less, the MAE (mean absolute error) was 0.824 h/day or less, the RE (relative error) was 0.150 or lower, and the value of d was 0.963 or greater, which validated that the proposed method can effectively predict daily sunshine duration. These equations can also provide higher precision estimates of regional-scale sunshine duration. This was demonstrated by comparing, for the entire study region, the spatial distribution of sunshine duration estimated from season-based equations with results from three different interpolation methods based on ground observations. Overall, the study confirms that total cloud amount measures from a geostationary Satellite can be used to successfully estimate sunshine duration.
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a method to estimate sunshine duration using cloud classification data from a geostationary Meteorological Satellite fy 2d over the heihe river basin
Sensors, 2016Co-Authors: Shufu Liu, Weiwei Zhu, Nana Yan, Qiang XingAbstract:Sunshine duration is an important variable that is widely used in atmospheric energy balance studies, analysis of the thermal loadings on buildings, climate research, and the evaluation of agricultural resources. In most cases, it is calculated using an interpolation method based on regional-scale Meteorological data from field stations. Accurate values in the field are difficult to obtain without ground measurements. In this paper, a Satellite-based method to estimate sunshine duration is introduced and applied over the Heihe River Basin. This method is based on hourly cloud classification product data from the FY-2D geostationary Meteorological Satellite (FY-2D). A new index—FY-2D cloud type sunshine factor—is proposed, and the Shuffled Complex Evolution Algorithm (SCE-UA) was used to calibrate sunshine factors from different coverage types based on ground measurement data from the Heihe River Basin in 2007. The estimated sunshine duration from the proposed new algorithm was validated with ground observation data for 12 months in 2008, and the spatial distribution was compared with the results of an interpolation method over the Heihe River Basin. The study demonstrates that geostationary Satellite data can be used to successfully estimate sunshine duration. Potential applications include climate research, energy balance studies, and global estimations of evapotranspiration.
Donald J Cavalieri - One of the best experts on this subject based on the ideXlab platform.
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aircraft active and passive microwave validation of sea ice concentration from the defense Meteorological Satellite program special sensor microwave imager
Journal of Geophysical Research, 1991Co-Authors: Donald J Cavalieri, J P Crawford, M R Drinkwater, Duane T Eppler, L D Farmer, R R Jentz, C C WackermanAbstract: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.