Special Sensor Microwave Imager Research Articles

Comparison of global observations and trends of total precipitable water derived from microwave radiometers and COSMIC radio occultation from 2006 to 2013

Abstract. We compare atmospheric total precipitable water (TPW) derived from the SSM/I (Special Sensor Microwave Imager) and SSMIS (Special Sensor Microwave Imager/Sounder) radiometers and WindSat to collocated TPW estimates derived from COSMIC (Constellation System for Meteorology, Ionosphere, and Climate) radio occultation (RO) under clear and cloudy conditions over the oceans from June 2006 to December 2013. Results show that the mean microwave (MW) radiometer – COSMIC TPW differences range from 0.06 to 0.18 mm for clear skies, from 0.79 to 0.96 mm for cloudy skies, from 0.46 to 0.49 mm for cloudy but non-precipitating conditions, and from 1.64 to 1.88 mm for precipitating conditions. Because RO measurements are not significantly affected by clouds and precipitation, the biases mainly result from MW retrieval uncertainties under cloudy and precipitating conditions. All COSMIC and MW radiometers detect a positive TPW trend over these 8 years. The trend using all COSMIC observations collocated with MW pixels for this data set is 1.79 mm decade−1, with a 95 % confidence interval of (0.96, 2.63), which is in close agreement with the trend estimated by the collocated MW observations (1.78 mm decade−1 with a 95 % confidence interval of 0.94, 2.62). The sample of MW and RO pairs used in this study is highly biased toward middle latitudes (40–60∘ N and 40–65∘ S), and thus these trends are not representative of global average trends. However, they are representative of the latitudes of extratropical storm tracks and the trend values are approximately 4 to 6 times the global average trends, which are approximately 0.3 mm decade−1. In addition, the close agreement of these two trends from independent observations, which represent an increase in TPW in our data set of about 6.9 %, are a strong indication of the positive water vapor–temperature feedback on a warming planet in regions where precipitation from extratropical storms is already large.

2014 summer Arctic sea ice thickness and concentration from shipborne observations

ABSTRACTA series of shipborne sea ice observations were performed during the Chinese National Arctic Research Expedition in the Pacific Arctic sector between 2 August 2014 and 1 September 2014. Undeformed sea ice thickness (SIT) as well as area fractions of open water, melt pond, and sea ice (Aw, Ap, and Ai) were monitored using downward-oriented and oblique-oriented cameras. The results show that SIT varied between 20 and 220 cm throughout the whole cruise, with the average and standard deviation equaling 104.9 and 29.1 cm, respectively. Mean Aw and Ai were 0.52 and 0.44 in the marginal ice zone, respectively, while mean Aw decreased to 0.23 and mean Ai increased to 0.73 in the pack ice zone. Limited variation between 0 and 0.32 in Ap was seen throughout the whole cruise. Shipborne sea ice concentration was then rectified and mapped across a large transect to validate estimates derived from the satellite sensors Special Sensor Microwave Imager/Sounder (SSMIS) (25 km) and AMSR2 (25 km). Overestimations were 9.5% and 9.9% for SSMIS and AMSR2 compared with measurements, respectively. The mean areal broadband surface albedo based on shipborne survey increased from 0.07 to 0.66 along the transect between 72°N and 81°N.

Inter-Calibration of Passive Microwave Satellite Brightness Temperatures Observed by F13 SSM/I and F17 SSMIS for the Retrieval of Snow Depth on Arctic First-Year Sea Ice

Passive microwave satellite brightness temperatures (TB) that were observed by the F13 Special Sensor Microwave/Imager (SSM/I) and the subsequent F17 Special Sensor Microwave Imager/Sounder (SSMIS) were inter-calibrated using empirical relationship models during their overlap period. Snow depth (SD) on the Arctic first-year sea ice was further retrieved. The SDs derived from F17 TB and F13C TB which were calibrated F17 TB using F13 TB as the baseline were then compared and evaluated against in situ SD measurements based on the Operational IceBridge (OIB) airborne observations from 2009 to 2013. Results show that Cavalieri inter-calibration models (CA models) perform smaller root mean square error (RMSE) than Dai inter-calibration models (DA models), and the standard deviation of OIB SDs in the 25 km pixels is around 6 cm on first-year sea ice. Moreover, the SDs derived from the calibrated F17 TB using F13 TB as the baseline were in better agreement than the F17 SDs as compared with OIB SDs, with the biases of −2 cm (RMSE of 5 cm) and −9 cm (RMSE of 10 cm), respectively. We conclude that TB observations from F17 SSMIS calibrated to F13 SSM/I as the baseline should be recommended when performing the sensors’ biases correction for SD purpose based on the existing algorithm. These findings could serve as a reference for generating more consistent and reliable TB, which could help to improve the retrieval and analysis of long-term snow depth on the Arctic first-year sea ice.

Comparisons of remote sensing and reanalysis soil moisture products over the Tibetan Plateau, China

The availability of reliable long-term and large scale soil moisture observation datasets over the Tibetan Plateau has been of more concern in recent years. It is therefore necessary to verify any such data to have a better guide to their usage. In this study, we have compared satellite soil moisture retrievals of the Special Sensor Microwave Imager (SSM/I), with ERA-Interim/Land and Global Land Data Assimilation System (GLDAS-1) soil moisture products over the Tibetan Plateau. They are found to be largely different from each other regarding the soil moisture magnitudes in spring, summer and autumn, with those of the ERA-Interim/Land particularly higher than the other datasets in the eastern plateau. The SSM/I and the ERA-Interim/Land data are in good agreement considering their seasonal dynamics, which are however considerably different from the GLDAS-1 models (CLM, Noah, Mosaic and VIC). For interannual variations, it is striking to find strong negative correlations (R<−0.6) between the SSM/I and the ERA-Interim/Land data in spring and autumn, especially over the central plateau. The SSM/I data is found to have also strong negative correlations (R<−0.4) with data of the four GLDAS-1 models in the eastern plateau in spring; and such negative correlations appear in central and western plateau in autumn. In summer, the SSM/I data appears to be positively correlated with the ERA-Interim/Land and all the GLDAS-1 data over the plateau. The negative correlations indicate these data have quite different temporal dynamics for their interannual variations, especially in spring and autumn. A look at the spatial variations revealed that the SSM/I data shows a north-south seesaw pattern in all seasons. The GLDAS-1 Mosaic, Noah and CLM data are able to represent this seesaw pattern principally, which however, can hardly be seen in the ERA-Interim/Land and the GLDAS-1 VIC data. Our results have indicated significant uncertainties in the soil moisture data from remote sensing and reanalysis sources; and we have to use such data with great care.

Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images

Surface snowmelt causes changes in mass and energy balance, and endangers the stabilities of the ice shelves in the Antarctic Peninsula (AP). The dynamic changes of the snow and ice conditions in the AP were observed by Sentinel-1 images with a spatial resolution of 40 m in this study. Snowmelt detected by the special sensor microwave/imager (SSM/I) is used to study the relationship between summer snowmelt and winter synthetic aperture radar (SAR) backscatter. Radar glacier zones (RGZs) classifications were conducted based on their differences in liquid snow content, snow grain size, and the relative elevations. We developed a practical method based on the simulations of a microwave scattering model to classify RGZs by using Sentinel-1 images in the AP. The summer snowmelt detected by SSM/I and Sentinel-1 data are compared between 2014 and 2015. The SSM/I-derived melting days is used to validate the winter dry snow line (DSL). RGZs derived from Sentinel-1 images suggest that snowmelt expanded from inland of the Larsen C Ice Shelf to the coastal area, whereas an opposite direction was found in the George VI Ice Shelf. The long melting season in the grounding zone of the Larsen C Ice Shelf may result from the adiabatically-dried föhn winds on the east side of the AP. As the uppermost limit of summer snowmelt, DSL was mapped based on the winter Sentinel-1 mosaic of the AP. Compared with the SSM/I-derived melting days, the winter DSL mainly distributed in the areas melted for one to three days in summer. DSL elevations on the Palmer Land increased from south to north.

Maximum temperature drove snow cover expansion from the Arctic, 2000\u20132008

Investigating annual phenology of snow cover around the circumpolar Arctic is of significance for better grasping the effect of environment variation on global climate change, and previous studies found that temperature is the kernel climate feature interlinking with snow onset. However, how temperature closely drives snow cover expansion has not been fully exploited. Our analysis based on the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) (i.e., SSM/I-SSMIS) data during 2000–2008 showed that the snow onset date (Do) was primarily driven by the maximum temperature approximately at the 22nd day in advance (termed as the optimal period, OP) in the Northern Hemisphere. Specifically, the spatial patterns of the Do trends are similar with the previous finding, e.g., east Europe and west Asia exhibiting the most notable Do delay, and the OP days in latitude show the principal trend of first decreasing and then increasing. These characteristics can be attributed to the variation of the maximum temperature feature in latitude. Overall, the derivation of the statistical rules of temperature driving snow cover expansion from the Arctic facilitates predicting the occurrence of snow and understanding various terrestrial processes.

Analysis and evaluation of WRF microphysical schemes for deep moist convection over south-eastern South America (SESA) using microwave satellite observations and radiative transfer simulations

Abstract. In the present study, three meteorological events of extreme deep moist convection, characteristic of south-eastern South America, are considered to conduct a systematic evaluation of the microphysical parameterizations available in the Weather Research and Forecasting (WRF) model by undertaking a direct comparison between satellite-based simulated and observed microwave radiances. A research radiative transfer model, the Atmospheric Radiative Transfer Simulator (ARTS), is coupled with the WRF model under three different microphysical parameterizations (WSM6, WDM6 and Thompson schemes). Microwave radiometry has shown a promising ability in the characterization of frozen hydrometeors. At high microwave frequencies, however, frozen hydrometeors significantly scatter radiation, and the relationship between radiation and hydrometeor populations becomes very complex. The main difficulty in microwave remote sensing of frozen hydrometeor characterization is correctly characterizing this scattering signal due to the complex and variable nature of the size, composition and shape of frozen hydrometeors. The present study further aims at improving the understanding of frozen hydrometeor optical properties characteristic of deep moist convection events in south-eastern South America. In the present study, bulk optical properties are computed by integrating the single-scattering properties of the Liu(2008) discrete dipole approximation (DDA) single-scattering database across the particle size distributions parameterized by the different WRF schemes in a consistent manner, introducing the equal mass approach. The equal mass approach consists of describing the optical properties of the WRF snow and graupel hydrometeors with the optical properties of habits in the DDA database whose dimensions might be different (Dmax′) but whose mass is conserved. The performance of the radiative transfer simulations is evaluated by comparing the simulations with the available coincident microwave observations up to 190 GHz (with observations from Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI), Microwave Humidity Sounder (MHS) and Special Sensor Microwave Imager/Sounder (SSMI/S)) using the χ2 test. Good agreement is obtained with all observations provided special care is taken to represent the scattering properties of the snow and graupel species.

Distributions of Tropical Precipitation Cluster Power and Their Changes under Global Warming. Part II: Long-Term Time Dependence in Coupled Model Intercomparison Project Phase 5 Models

Abstract Distributions of precipitation cluster power (latent heat release rate integrated over contiguous precipitating pixels) are examined in 1°–2°-resolution members of phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate model ensemble. These approximately reproduce the power-law range and large event cutoff seen in observations and the High Resolution Atmospheric Model (HiRAM) at 0.25°–0.5° in Part I. Under the representative concentration pathway 8.5 (RCP8.5) global warming scenario, the change in the probability of the most intense storm clusters appears in all models and is consistent with HiRAM output, increasing by up to an order of magnitude relative to historical climate. For the three models in the ensemble with continuous time series of high-resolution output, there is substantial variability on when these probability increases for the most powerful storm clusters become detectable, ranging from detectable within the observational period to statistically significant trends emerging only after 2050. A similar analysis of National Centers for Environmental Prediction (NCEP)–U.S. Department of Energy (DOE) AMIP-II reanalysis and Special Sensor Microwave Imager and Imager/Sounder (SSM/I and SSMIS) rain-rate retrievals in the recent observational record does not yield reliable evidence of trends in high power cluster probabilities at this time. However, the results suggest that maintaining a consistent set of overlapping satellite instrumentation with improvements to SSM/I–SSMIS rain-rate retrieval intercalibrations would be useful for detecting trends in this important tail behavior within the next couple of decades.

Sea-Ice Production in Antarctic Coastal Polynyas Estimated From AMSR2 Data and Its Validation Using AMSR-E and SSM/I-SSMIS Data

Antarctic coastal polynyas are very high sea-ice production areas. The resultant large amount of brine rejection leads to the formation of dense water. The dense water forms Antarctic bottom water, which is the densest water in the global overturning circulation and a key player in climate change as a significant sink for heat and carbon dioxide. In this study, an algorithm was developed that uses Advanced Microwave Scanning Radiometer 2 (AMSR2) data (2012–present) to detect polynya area and estimates thin ice thickness by a method similar to that used to develop the algorithm for Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data. Landfast sea-ice areas were also detected using AMSR2 data. Ice production in the polynyas was estimated by a heat flux calculation using AMSR2 sea-ice data. In four major polynyas, AMSR2 ice production was compared with AMSR-E (2003–2011) ice production through comparison of them with Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) ice production. The comparison confirmed that the ice production from AMSR-E/2 data, which have higher spatial resolution than SSM/I-SSMIS data, can be used to analyze time series covering more than 10 years. For example, maps of annual ice production based on AMSR-E/2 data revealed detailed changes of the Mertz Polynya, where the ice production decreased significantly after the Mertz Glacier Tongue calving in 2010. Continuous monitoring of the coastal polynyas by the AMSR series sensors is essential for climate-change-related analyses in the Antarctic Ocean.

Precipitation Estimates from SMOS Sea‐Surface Salinity

Two L‐Band (1.4 GHz) microwave radiometer missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) currently provide sea‐surface salinity (SSS) measurements. At this frequency, salinity is measured in the first centimetre below the sea surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS SSS measurements and Special Sensor Microwave Imager/Sounder (SSMIS) RR. It is then used to develop an algorithm to estimate RR from SMOS SSS measurements. A heuristic function is developed to correct the SMOS‐estimated negative RR due to measurement noise. Correlation between SMOS and SSMIS RR and between SMOS and Integrated MultisatellitE Retrievals for GPM (IMERG) RR are high when SMOS and SSMIS passes are less than 15 min apart (r = 0.7 at 1° × 1° resolution), showing a good quality of SMOS RR retrievals. When the time shift between SMOS and SSMIS passes increases, the correlation between SMOS and IMERG RR diminishes. This suggests that L‐band radiometry can provide information complementary to GPM missions to improve RR products interpolated at high temporal resolution. The retrieval is successfully tested on SMAP SSS. We also check that our algorithm provides reliable estimates of RR when averaged at a monthly time‐scale.

Capability of the SMAP Mission to Measure Ocean Surface Winds in Storms

AbstractThe National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission was launched in January 2015 and has been providing science data since April 2015. Though designed to measure soil moisture, the SMAP radiometer has an excellent capability to measure ocean winds in storms at a resolution of 40 km with a swath width of 1,000 km. SMAP radiometer channels operate at a very low microwave frequency (L band, 1.41 GHz, 21.4 cm), which has good sensitivity to ocean surface wind speed even in very high winds and with very little impact by rain. This gives SMAP a distinct advantage over many spaceborne ocean wind sensors such as C-band [Advanced Scatterometer (ASCAT)] or Ku-band [Rapid Scatterometer (RapidScat)] scatterometers and radiometers operating at higher frequencies [Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), WindSat, Advanced Microwave Scanning Radiometer (AMSR), and Global Precipitation Measurement (GPM) Microwave Imager (GMI)], which either lose sensitivity at very high winds or degrade in rainy conditions. This article discusses the major features of a new ocean wind vector retrieval algorithm designed for SMAP. We compare SMAP wind fields in recent intense tropical cyclones with wind measurements from current scatterometer missions as well as WindSat. The most important validation source in hurricanes is the airborne stepped frequency microwave radiometer (SFMR), whose wind speeds are matched with SMAP in space and time. A comparison between SMAP and SFMR winds for eight storms in 2015, including Patricia, one of the strongest hurricanes ever recorded, shows excellent agreement up to 65 m s–1 without degradation in rain.

Monitoring ice variations in Qinghai Lake from 1979 to 2016 using passive microwave remote sensing data

Lake ice is a sensitive indicator of climate change. Based on the disparities between the brightness temperatures of lake ice and water, passive microwave data can be used to monitor the ice variations of a lake. With focus on the analysis of long time series variability of lake ice, this study extracts four characteristic dates related to lake ice (the annual freeze start, freeze completion, ablation start and ablation completion dates) for Qinghai Lake from 1979 to 2016 using Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) passive microwave brightness temperature data. The corresponding freezing duration, ablation duration, complete freezing duration and ice coverage duration are calculated. Applying Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products, the accuracy of the results derived from passive microwave data is validated. The validation analysis shows a strong agreement (R2 ranges from 0.70 to 0.85, mean absolute error (MAE) ranges from 2.25 to 3.94days) in the freeze start, ablation start, and ablation completion dates derived from the MODIS data and passive microwave data; the ice coverage duration also has a small error (relative error (RE)=2.95%, MAE=3.13days), suggesting that the results obtained from passive microwave data are reliable. The results show that the freezing dates of Qinghai Lake have been delayed and the ablation dates have advanced. Over 38years, the freeze start date and freeze completion date have been pushed back by 6.16days and 2.27days, respectively, while the ablation start date and ablation completion date have advanced by 11.24days and 14.09days, respectively. The freezing duration and ablation duration have shortened by 3.89days and 2.85days, respectively, and the complete freezing duration and ice coverage duration have shortened by 14.84days and 21.21days, respectively. There is a significant negative correlation between the ice coverage duration and the mean air temperature in winter.

Antarctic snow melt detection based on the synergy of SSM/I and QuikSCAT

AbstractMicrowave radiometer SSM/I (Special Sensor Microwave Imager) data and scatterometer QuikSCAT (Quick Scatterometer) data have been widely used for near-surface snow melt detection based on their sensitivity to liquid water present in snow. The SSM/I data have high reliability and the QuikSCAT data have high spatial resolution. In order to improve the accuracy of Antarctic near-surface snow melt detection, we propose a new method based on the synergy of SSM/I and QuikSCAT data, i.e. the snow melt physical model incorporates the complementary advantages of both datasets. Based on comparisons with temperature data from three automatic weather stations, the proposed algorithm improved the accuracy of snow melt detection. The algorithm could also be applied to other regions, which would provide further evidence to support its use and additional data to document changes in the Antarctic due to global climate change.

Fusion of sea surface wind vector data acquired by multi-source active and passive sensors in China sea

ABSTRACTThis work is the first to analyse the sea surface wind vector (SSWV) data acquisition capabilities of eight satellites carrying microwave scatterometer (scanning scatterometer carried by Haiyang satellite 2A, advanced scatterometer carried by Metop satellite A, advanced scatterometer carried by Metop satellite B and scanning scatterometer carried by Oceansat satellite 2) or radiometers (Special Sensor Microwave Imager carried by Meteorological Satellite Program satellites F15 and F17, advanced microwave scanning radiometer 2 carried by GCOM-W1 satellite, and windsat polarimetric radiometer carried by Coriolis satellite) and investigate a SSWV fusion algorithm for active and passive remote-sensing data. We found that combining observations of the eight satellites can provide an SSWV data product with spatial resolution of 25 km × 25 km and temporal resolution of 3 h. Sea surface wind speed and direction data were obtained from multi-source active and passive sensors using a spatiotemporally weighted fusion algorithm. An adaptive sliding window was introduced for calculating effective observation data within spatial/temporal radii, which can effectively improve calculation efficiency for wind field fusion. Comparing the fused and buoy observation results, the root-mean-square errors of the wind direction and speed were 20.6° and 1.2 m s–1, respectively, indicating that the fusion results can meet most application requirements for wind vector. Meanwhile, the space coverage, accuracy of merged wind speed and wind direction can be improved comparing to a single sensor.

A multi-sensor data-driven methodology for all-sky passive microwave inundation retrieval

Abstract. We present a multi-sensor Bayesian passive microwave retrieval algorithm for flood inundation mapping at high spatial and temporal resolutions. The algorithm takes advantage of observations from multiple sensors in optical, short-infrared, and microwave bands, thereby allowing for detection and mapping of the sub-pixel fraction of inundated areas under almost all-sky conditions. The method relies on a nearest-neighbor search and a modern sparsity-promoting inversion method that make use of an a priori dataset in the form of two joint dictionaries. These dictionaries contain almost overlapping observations by the Special Sensor Microwave Imager and Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F17 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites. Evaluation of the retrieval algorithm over the Mekong Delta shows that it is capable of capturing to a good degree the inundation diurnal variability due to localized convective precipitation. At longer timescales, the results demonstrate consistency with the ground-based water level observations, denoting that the method is properly capturing inundation seasonal patterns in response to regional monsoonal rain. The calculated Euclidean distance, rank-correlation, and also copula quantile analysis demonstrate a good agreement between the outputs of the algorithm and the observed water levels at monthly and daily timescales. The current inundation products are at a resolution of 12.5 km and taken twice per day, but a higher resolution (order of 5 km and every 3 h) can be achieved using the same algorithm with the dictionary populated by the Global Precipitation Mission (GPM) Microwave Imager (GMI) products.

Sea ice extent retrieval with HY-2A scatterometer data and its assessment

A sea ice extent retrieval algorithm over the polar area based on scatterometer data of HY-2A satellite has been established. Four parameters are used for distinguishing between sea ice and ocean with Fisher's linear discriminant analysis method. The method is used to generate polar sea ice extent maps of the Arctic and Antarctic regions of the full 2013–2014 from the scatterometer aboard HY-2A (HY-2A-SCAT) backscatter data. The time series of the ice mapped imagery shows ice edge evolution and indicates a similar seasonal change trend with total ice area from DMSP-F17 Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data. For both hemispheres, the HY-2A-SCAT extent correlates very well with SSMIS 15% extent for the whole year period. Compared with Synthetic Aperture Radar (SAR) imagery, the HY-2A-SCAT ice extent shows good correlation with the Sentinel-1 SAR ice edge. Over some ice edge area, the difference is very evident because sea ice edges can be very dynamic and move several kilometers in a single day.

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.

Surface Emissivity at Microwaves to Millimeter Waves over Polar Regions: Parameterization and Evaluation with Aircraft Experiments

AbstractThe Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2) is linked to a climatology of monthly emissivity estimates and provides a parameterization of the surface emissivity up to 700 GHz, in the framework of the preparation for the Ice Cloud Imager (ICI) on board the Meteorological Operational Satellite Second Generation (MetOp-SG). It is an updated version of the Tool to Estimate Land Surface Emissivities at Microwave Frequencies (TELSEM; Aires et al. 2011). This study presents the parameterization of continental snow and ice and sea ice emissivities in TELSEM2. It relies upon satellite-derived emissivities up to 200 GHz, and it is anchored to the Special Sensor Microwave Imager (SSM/I) TELSEM monthly climatology dataset (19–85 GHz). Emissivities from Météo-France and the National Oceanic and Atmospheric Administration (NOAA) at frequencies up to 190 GHz were used, calculated from the Special Sensor Microwave Imager/Sounder (SSMIS) and the Advanced Microwave Sounding Unit-B (AMSU-B) observations. TELSEM2 has been evaluated up to 325 GHz with the observations of the International Submillimeter Airborne Radiometer (ISMAR) and the Microwave Airborne Radiometer Scanning System (MARSS), which were operated on board the Facility for Airborne Atmospheric Measurements (FAAM) aircraft during the Cold-Air Outbreak and Submillimeter Ice Cloud Study (COSMICS) campaign over Greenland. Above continental snow and ice, TELSEM2 is very consistent with the aircraft estimates in spatially homogeneous regions, especially at 89 and 157 GHz. Over sea ice, the aircraft estimates are very variable spatially and temporally, and the comparisons with the TELSEM2 were not conclusive. TELSEM2 will be distributed in the new version of the RTTOV radiative transfer community code, to be available in 2017.

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.

An extended global Earth system data record on daily landscape freeze–thaw status determined from satellite passive microwave remote sensing

Abstract. The landscape freeze–thaw (FT) signal determined from satellite microwave brightness temperature (Tb) observations has been widely used to define frozen temperature controls on land surface water mobility and ecological processes. Calibrated 37 GHz Tb retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and SSM/I Sounder (SSMIS) were used to produce a consistent and continuous global daily data record of landscape FT status at 25 km grid cell resolution. The resulting FT Earth system data record (FT-ESDR) is derived from a refined classification algorithm and extends over a larger domain and longer period (1979–2014) than prior FT-ESDR releases. The global domain encompasses all land areas affected by seasonal frozen temperatures, including urban, snow- and ice-dominant and barren land, which were not represented by prior FT-ESDR versions. The FT retrieval is obtained using a modified seasonal threshold algorithm (MSTA) that classifies daily Tb variations in relation to grid-cell-wise FT thresholds calibrated using surface air temperature data from model reanalysis. The resulting FT record shows respective mean annual spatial classification accuracies of 90.3 and 84.3 % for evening (PM) and morning (AM) overpass retrievals relative to global weather station measurements. Detailed data quality metrics are derived characterizing the effects of sub-grid-scale open water and terrain heterogeneity, as well as algorithm uncertainties on FT classification accuracy. The FT-ESDR results are also verified against other independent cryospheric data, including in situ lake and river ice phenology, and satellite observations of Greenland surface melt. The expanded FT-ESDR enables new investigations encompassing snow- and ice-dominant land areas, while the longer record and favorable accuracy allow for refined global change assessments that can better distinguish transient weather extremes, landscape phenological shifts, and climate anomalies from longer-term trends extending over multiple decades. The dataset is freely available online (doi:10.5067/MEASURES/CRYOSPHERE/nsidc-0477.003).