Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2

For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records

Comparison of Remotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas

In order to obtain accurate prediction of ocean wind energy, long term data are needed. However, one data sources might not able to provide long duration data. Therefore, the data need to be combined with other sources of data. However, before combining the data, it is important to compare and validate them to confirm their accuracy. In the present study, wind speed data collected by QuikScat and SSM/I (SSMIS) missions are compared and analyzed. QuikScat data were collected by a satellite with the same name, while Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data are processed and offered by Remote Sensing System (RSS). SSM/I (SSMIS) are passive microwave radiometers carried onboard Defense Meteorological Satellite Program (DMSP). For the comparison, 5 (five) arbitrary positions over Sulawesi and Maluku islands sea areas are chosen for the analyses. For the evaluation purposes, beside time series of daily data from several chosen positions in research location, several statistical parameters are also computed and compared such as mean, standard deviation, root mean square (RMS), correlation coefficient.

RETRIEVAL OF ICE CLOUD PARAMETERS USING DMSP SPECIAL SENSOR MICROWAVE IMAGER/SOUNDER

Clouds exert a profound influence on both the water balance of the atmosphere and the earth's radiation budget (Stephens 2005; Stephens and Kummerow 2007). Among the global distribution, 30% of them are ice clouds (Riedi et al. 2000). It is important to improve our knowledge of the ice cloud properties in order to determine their influence to the global ecosystem. For ice clouds with millimeter-size ice particles, which are generally found in anvil cirrus and deep convections, microwave and millimeter wave length satellite measurements are suitable for the ice cloud microphysical property retrieval because of its strong ability to penetrate deeper into dense ice clouds. For these types of ice clouds, brightness temperatures at the top of the atmosphere are analytically derived as a function of vertically integrated ice water content (i.e. ice water path), effective particle diameter, and bulk volume density. In general, three brightness temperature measurements are needed to retrieve the three ice cloud microphysical parameters. A two-stream radiative transfer theory was applied to data from the Advanced Microwave Sounding Unit (AMSU) and the Moisture Humidity Sensor (MHS) in order to generate global ice water paths operationally. This research further applied the model and theory to derive ice water path (IWP) from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite. Compared to AMSU/MHS, which have field of views (FOV) varying with scan position, SSMIS scans the Earth's atmosphere at a constant viewing angle of 53o and therefore offers a uniform FOV within each scan. This unique feature allows for improved global mapping and monitoring of ice clouds so that a more accurate and realistic IWP and ice particle effective diameter distribution is expected. A direct application of SSMIS-derived ice water path is its relationship with surface rain rate as derived previously for AMSU and MHS instruments. Here, SSMIS-derived rain rate was compared to the AMSU and MHS rainfall products and hourly synthetic precipitation observations from rain gauges and surface radar. Results show that SSMIS surface precipitation distribution is spatially consistent and does not have apparent artificial boundary near coastal zones as previously seen in other algorithms. Also, the ice water path associated with a severe storm reasonably delineates the strong convective precipitation areas and has a spatial variation consistent with surface precipitation. From retrieved instantaneous surface precipitation, a tropical and subtropical oceanic precipitation anomaly time series is constructed from 5 year's worth (2005-2009) of SSMIS data. This data record is also linked to the previous constructed SSM/I 15-year (1992-2006) data record to provide a longer term climate study by satellite observations. In future studies, refined algorithms for the estimate of ice cloud base temperature and ice particle bulk volume density are going to be developed to improve the accuracy of IWP retrieval under various cloud vertical distributions. Meanwhile, a better inter-sensor cross calibration scheme is the key to make satellite measurements more useful in climate change study

Characterization of Tropical Cyclone Intensity Using Microwave Imagery

In the absence of wind speed data from aircraft reconnaissance of tropical cyclones (TCs), analysts rely on remote sensing tools to estimate TC intensity. For over 40 years, the Dvorak technique has been applied to estimate intensity using visible and infrared (IR) satellite imagery, but its accuracy is sometimes limited when the radiative effects of high clouds obscure the TC convective structure below. Microwave imagery highlights areas of precipitation and deep convection revealing different patterns than visible and IR imagery. This study explores application of machine learning algorithms to identify patterns in microwave imagery to infer storm intensity, particularly focusing on weaker storms where other analysis methods struggle. An analysis of 91 GHz Special Sensor Microwave Imager/Sensor imagery onboard various Defense Meteorological Satellite Program assets from February 2006 to 2017 is presented. Incorporating pattern recognition methods into the current analysis process at the Joint Typhoon Warning Center has the potential to significantly improve TC intensity estimates across all basins of responsibility

Multi-Decadal Variability of Polynya Characteristics and Ice Production in the North Water Polynya by Means of Passive Microwave and Thermal Infrared Satellite Imagery

The North Water (NOW) Polynya is a regularly-forming area of open-water and thin-ice, located between northwestern Greenland and Ellesmere Island (Canada) at the northern tip of Baffin Bay. Due to its large spatial extent, it is of high importance for a variety of physical and biological processes, especially in wintertime. Here, we present a long-term remote sensing study for the winter seasons 1978/1979 to 2014/2015. Polynya characteristics are inferred from (1) sea ice concentrations and brightness temperatures from passive microwave satellite sensors (Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager/Sounder (SSM/I-SSMIS)) and (2) thin-ice thickness distributions, which are calculated using MODIS ice-surface temperatures and European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis data in a 1D thermodynamic energy-balance model. Daily ice production rates are retrieved for each winter season from 2002/2003 to 2014/2015, assuming that all heat loss at the ice surface is balanced by ice growth. Two different cloud-cover correction schemes are applied on daily polynya area and ice production values to account for cloud gaps in the MODIS composites. Our results indicate that the NOW polynya experienced significant seasonal changes over the last three decades considering the overall frequency of polynya occurrences, as well as their spatial extent. In the 1980s, there were prolonged periods of a more or less closed ice cover in northern Baffin Bay in winter. This changed towards an average opening on more than 85% of the days between November and March during the last decade. Noticeably, the sea ice cover in the NOW polynya region shows signs of a later-appearing fall freeze-up, starting in the late 1990s. Different methods to obtain daily polynya area using passive microwave AMSR-E/AMSR2 data and SSM/I-SSMIS data were applied. A comparison with MODIS data (thin-ice thickness ≤ 20 cm) shows that the wintertime polynya area estimates derived by MODIS are about 30 to 40% higher than those derived using the polynya signature simulation method (PSSM) with AMSR-E data. In turn, the difference in polynya area between PSSM and a sea ice concentration (SIC) threshold of 70% is fairly low (approximately 10%) when applied to AMSR-E data. For the coarse-resolution SSM/I-SSMIS data, this difference is much larger, particularly in November and December. Instead of a sea ice concentration threshold, the PSSM method should be used for SSM/I-SSMIS data. Depending on the type of cloud-cover correction, the calculated ice production based on MODIS data reaches an average value of 264.4 ± 65.1 km 3 to 275.7 ± 67.4 km 3 (2002/2003 to 2014/2015) and shows a high interannual variability. Our achieved long-term results underline the major importance of the NOW polynya considering its influence on Arctic ice production and associated atmosphere/ocean processes

On-orbit Inter-satellite Radiometric Calibration of Cross-track Scanning Microwave Radiometers

This dissertation concerns the development of an improved algorithm for the inter-satellite radiometric calibration (XCAL) for cross track scanning microwave radiometers in support of NASA\u27s Global Precipitation Mission (GPM). This research extends previous XCAL work to assess the robustness of the CFRSL double difference technique for sounder X-CAL. In this work, using a two-year of observations, we present a statistical analysis of radiometric biases performed over time and viewing geometry. In theory, it is possible to apply the same X-CAL procedure developed for conical-scanning radiometers to cross-track scanners; however the implementation is generally more tedious. For example, with the cross-track scan angle, there is a strong response in the observed Tb due to changes in the atmosphere slant path and surface emissivity with the Earth incidence angle. For ocean scenes this is trivial; however for land scenes there is imperfect knowledge of polarized emissivity. However, for the sounder channels the surface emissivity is not the dominant component of top-of-the-atmosphere Tb, which is a mitigating factor. Also, cross-track scanners introduce changes in the radiometer antenna observed polarization with scan angle. The resulting observation is a mixture of un-polarized atmospheric emissions and vertical and horizontal polarized surface emissions. The degree of polarization mixing is known from geometry; however, reasonable estimates of the surface emissivity are required, which complicate over land comparisons. Finally, the IFOV size monotonically increases over the cross-track scan. Thus, when inter-comparing cross-track scanning radiometers, it will be necessary to carefully consider these effects when performing the double difference procedure

Microwave Brightness Temperature Characteristics of Three Strong Earthquakes in Sichuan Province, China

Passive microwave remote sensing technology is an effective means to identify the thermal anomalies associated with earthquakes due to its penetrating capability through clouds compared with infrared sensors. However, observed microwave brightness temperature is strongly influenced by soil moisture and other surface parameters. In the present article, the segmented threshold method has been proposed to detect anomalous microwave brightness temperature associated with the strong earthquakes occurred in Sichuan province, China, an earthquake-prone area with high soil moisture. The index of microwave radiation anomaly (IMRA) computed by the proposed method is found to enhance prior to the three strong earthquakes, 2008 Wenchuan (M = 7.8), 2013 Lushan (M = 6.6), and 2017 Jiuzhaigou (M = 6.5), occurred during 2008-2018 using the Defense Meteorological Space Program Special Sensor Microwave Imager/Sounder F17 satellite data. Our results show that the microwave brightness temperature anomalies appeared about two months prior to the three strong earthquakes. For the Wenchuan and Lushan earthquakes, the enhanced IMRA distributed along the main fault, which is consistent with the variations of our earlier studies of the 1997 Manyi (M = 7.5) and the 2001 Kokoxili (M = 7.8) earthquakes in the region with low soil moisture. For the Jiuzhaigou earthquake, the anomalies distributed around the epicenter and do not indicate the seismogenic structure, which could be due to the presence of a blind fault. It should be noted that quantitative evaluation of IMRA is limited due to infrequent occurrence of earthquakes