scholarly journals Detecting cloud contamination in passive microwave satellite measurements over land

2019 ◽  
Vol 12 (3) ◽  
pp. 1531-1543 ◽  
Author(s):  
Samuel Favrichon ◽  
Catherine Prigent ◽  
Carlos Jimenez ◽  
Filipe Aires
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Cloud Formation ◽  
Temperature Estimation ◽  
Near Surface ◽  
Brightness Temperatures ◽  
Infrared Imager ◽  
Microwave Imager ◽  
Successive Generations ◽  
Global Precipitation

Abstract. Remotely sensed brightness temperatures from passive observations in the microwave (MW) range are used to retrieve various geophysical parameters, e.g. near-surface temperature. Cloud contamination, although less of an issue at MW than at visible to infrared wavelengths, may adversely affect retrieval quality, particularly in the presence of strong cloud formation (convective towers) or precipitation. To limit errors associated with cloud contamination, we present an index derived from stand-alone MW brightness temperature observations, which measure the probability of residual cloud contamination. The method uses a statistical neural network model trained with the Global Precipitation Microwave Imager (GMI) observations and a cloud classification from Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This index is available over land and ocean and is developed for multiple frequency ranges to be applicable to successive generations of MW imagers. The index confidence increases with the number of available frequencies and performs better over the ocean, as expected. In all cases, even for the more challenging radiometric signatures over land, the model reaches an accuracy of ≥70 % in detecting contaminated observations. Finally an application of this index is shown that eliminates grid cells unsuitable for land surface temperature estimation.

scholarly journals Detecting cloud contamination in passive microwave satellite measurements over land

2018 ◽  
Author(s):  
Samuel Favrichon ◽  
Catherine Prigent ◽  
Carlos Jimenez ◽  
Filipe Aires
Keyword(s):  
Surface Temperature ◽  
Brightness Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cloud Formation ◽  
Infrared Imager ◽  
Microwave Imager ◽  
Successive Generations ◽  
Statistical Neural Networks ◽  
Global Precipitation

Abstract. Multiple geophysical parameters such as land surface temperature, are estimated using Microwave (MW) remote sensed brightness temperature. It is known that clouds do not affect those measurement in the MWs as much as in Visible and Infrared (VIS/IR), but some contamination can still occur when strong cloud formation (i.e. convective towers) or precipitation are present. To limit errors associated to cloud contamination in the estimation of surface parameters, we build an index giving the confidence to have an observation clear from contamination using standalone MW brightness temperature measurements. The method developed uses a statistical neural networks model built upon the Global Precipitation Microwave Imager (GPM-GMI) observations, with cloud presence information taken from Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This index is available over land and ocean, and is developed for multiple frequency ranges to be applicable to successive generations of MW imagers (10 to 40 GHz, 10 to 100 GHz, 10 to 200 GHz). The index confidence increases with the number of channels available, and performs better over the ocean as expected. In all cases, even with a reduced number of information over land, the model reaches an accuracy > 70 %, in detecting contaminated observations. Finally an example application of this index to eliminate grid cells unsuitable for land surface temperature estimation is shown.

scholarly journals Inter-calibrating SMMR brightness temperatures over continental surfaces

2020 ◽  
Author(s):  
Samuel Favrichon ◽  
Carlos Jimenez ◽  
Catherine Prigent
Keyword(s):  
Land Surface ◽  
Microwave Remote Sensing ◽  
Calibration Method ◽  
Diurnal Cycles ◽  
Brightness Temperatures ◽  
Linear Correction ◽  
Long Time ◽  
Microwave Imager ◽  
Microwave Radiometers ◽  
Global Precipitation

Abstract. Microwave remote sensing can be used to monitor the time evolution of some key parameters over land, such as land surface temperature or surface water extent. Observations are made with instrument such as the Scanning Microwave Multichannel Radiometer (SMMR) before 1987, the Special Sensor Microwave/Imager (SSM/I) and the following Special Sensor Microwave Imager/Sounder (SSMIS) from 1987 and still operating, to the more recent Global Precipitation Mission Microwave Imager (GMI). As these instruments differ on some of their characteristics and use different calibration schemes, they need to be inter-calibrated before long time series products can be derived from the observations. Here an inter-calibration method is designed to remove major inconsistencies between the SMMR and other microwave radiometers for the 18 GHz and 37 GHz channels over continental surfaces. Because of a small overlap in observations and a ~6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from three years of GMI brightness temperatures are first calculated, and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data record of microwave brightness temperatures over continental surfaces.

scholarly journals Comparison of SLSTR Thermal Emissive Bands Clear-Sky Measurements with Those of Geostationary Imagers

2020 ◽  
Vol 12 (20) ◽  
pp. 3279
Author(s):  
Bingkun Luo ◽  
Peter J. Minnett
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Azimuth Angle ◽  
Sea Surface ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Infrared Imager ◽  
Earth Observation Satellite

The Sentinel-3 series satellites belong to the European Earth Observation satellite missions for supporting oceanography, land, and atmospheric studies. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites was designed to provide a significant improvement in remote sensing of skin sea surface temperature (SSTskin). The successful application of SLSTR-derived SSTskin fields depends on their accuracies. Based on sensor-dependent radiative transfer model simulations, geostationary Geostationary Operational Environmental Satellite (GOES-16) Advanced Baseline Imagers (ABI) and Meteosat Second Generation (MSG-4) Spinning Enhanced Visible and Infrared Imager (SEVIRI) brightness temperatures (BT) have been transformed to SLSTR equivalents to permit comparisons at the pixel level in three ocean regions. The results show the averaged BT differences are on the order of 0.1 K and the existence of small biases between them are likely due to the uncertainties in cloud masking, satellite view angle, solar azimuth angle, and reflected solar light. This study demonstrates the feasibility of combining SSTskin retrievals from SLSTR with those of ABI and SEVIRI.

scholarly journals Evaluating sources of an apparent cold bias in MODIS land surface temperatures in the St. Elias Mountains, Yukon, Canada

2021 ◽  
Author(s):  
Ingalise Kindstedt ◽  
Kristin Schild ◽  
Dominic Winski ◽  
Karl Kreutz ◽  
Luke Copland ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Cold Bias ◽  
Near Surface ◽  
Wind Speeds ◽  
Brightness Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Temperature Inversions

Abstract. Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have shown a cold bias relative to available weather station measurements, the source of which is unknown. Here, we show that the MODIS cold bias likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that a cold bias in remote sensing temperatures is present not only in MODIS LST products, but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat surface temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the cold bias was most pronounced in the winter (mean cold bias > 8 °C), and least pronounced in the spring and summer (mean cold bias < 2 °C). We also find this enhanced seasonal bias in MODIS brightness temperatures, before the incorporation of snow surface emissivity into the LST calculation. Finally, we find the MODIS cold bias to be consistent in magnitude and seasonal distribution with modeled temperature inversions, and to be most pronounced under conditions that facilitate near-surface inversions, namely low incoming solar radiation and wind speeds, at study sites Icefield Divide (60.68° N, 139.78° W, 2,603 m a.s.l) and Eclipse Icefield (60.84° N, 139.84° W, 3,017 m a.s.l.). These results demonstrate that efforts to improve the accuracy of MODIS LSTs should focus on understanding near-surface physical processes rather than refining the MODIS sensor or LST algorithm. In the absence of a physical correction for the cold bias, we apply a statistical correction, enabling the use of mean annual MODIS LSTs to qualitatively and quantitatively examine temperatures in the St. Elias Mountains and their relationship to melt and mass balance.

scholarly journals Satellite observations of surface temperature during the March 2015 total solar eclipse

2016 ◽  
Vol 374 (2077) ◽  
pp. 20150219 ◽  
Author(s):  
Elizabeth Good
Keyword(s):  
Surface Temperature ◽  
Solar Eclipse ◽  
Land Surface ◽  
Total Solar Eclipse ◽  
Atmospheric Effects ◽  
Near Surface ◽  
Full Characterization ◽  
Air Temperatures ◽  
Infrared Imager ◽  
Land Surface Temperatures

The behaviour of remotely sensed land surface temperatures (LSTs) from the spinning-enhanced visible and infrared imager (SEVIRI) during the total solar eclipse of 20 March 2015 is analysed over Europe. LST is found to drop by up to several degrees Celcius during the eclipse, with the minimum LST occurring just after the eclipse mid-point (median=+1.5 min). The drop in LST is typically larger than the drop in near-surface air temperatures reported elsewhere, and correlates with solar obscuration ( r =−0.47; larger obscuration = larger LST drop), eclipse duration ( r =−0.62; longer duration = larger LST drop) and time ( r =+0.37; earlier eclipse = larger LST drop). Locally, the LST drop is also correlated with vegetation (up to r =+0.6), with smaller LST drops occurring over more vegetated surfaces. The LSTs at locations near the coast and at higher elevation are also less affected by the eclipse. This study covers the largest area and uses the most observations of eclipse-induced surface temperature drops to date, and is the first full characterization of satellite LST during an eclipse (known to the author). The methods described could be applied to Geostationary Operational Environmental Satellite (GOES) LST data over North America during the August 2017 total solar eclipse. This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’.

scholarly journals Inter-calibrating SMMR brightness temperatures over continental surfaces

2020 ◽  
Vol 13 (10) ◽  
pp. 5481-5490
Author(s):  
Samuel Favrichon ◽  
Carlos Jimenez ◽  
Catherine Prigent
Keyword(s):  
Land Surface ◽  
Microwave Remote Sensing ◽  
Calibration Method ◽  
Diurnal Cycles ◽  
Brightness Temperatures ◽  
Linear Correction ◽  
Long Time ◽  
Microwave Imager ◽  
Microwave Radiometers ◽  
Global Precipitation

Abstract. Microwave remote sensing can be used to monitor the time evolution of some key parameters over land, such as land surface temperature or surface water extent. Observations are made with instruments, such as the Scanning Microwave Multichannel Radiometer (SMMR) before 1987, the Special Sensor Microwave/Imager (SSM/I) and the subsequent Special Sensor Microwave Imager/Sounder (SSMIS) from 1987 and still operating, and the more recent Global Precipitation Measurement Microwave Imager (GMI). As these instruments differ on some of their characteristics and use different calibration schemes, they need to be inter-calibrated before long-time-series products can be derived from the observations. Here an inter-calibration method is designed to remove major inconsistencies between the SMMR and other microwave radiometers for the 18 and 37 GHz channels over continental surfaces. Because of a small overlap in observations and a ∼6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from 3 years of GMI brightness temperatures are first calculated and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data records of microwave brightness temperatures over continental surfaces.

scholarly journals Daily Microwave-Derived Surface Temperature over Canada/Alaska

2007 ◽  
Vol 46 (5) ◽  
pp. 591-604 ◽  
Author(s):  
A. Mialon ◽  
A. Royer ◽  
M. Fily ◽  
G. Picard
Keyword(s):  
Surface Temperature ◽  
Confidence Level ◽  
Land Surface ◽  
Degree Days ◽  
Near Surface ◽  
Air Temperatures ◽  
Discontinuous Permafrost ◽  
Climate Evolution ◽  
Microwave Imager ◽  
Satellite Microwave Measurements

Abstract The land surface temperature variation over northern high latitudes in response to the increase in greenhouse gases is challenging because of the lack of meteorological stations. A new method to derive the surface temperature from satellite microwave measurements that improves the frequency of measurements relative to that of infrared data is presented. The daily Special Sensor Microwave Imager 25 km × 25 km Equal-Area Scalable Earth Grid (EASE-Grid) dataset provided by the National Snow and Ice Data Center in Boulder, Colorado, is processed to derive the surface temperature using the method proposed by Fily et al. A normalization approach based on the 40-yr ECMWF reanalysis (ERA-40; 2.5°) temperature diurnal cycle fitted for each pixel is applied to overcome the time acquisition variation of measurements as well as to interpolate missing data. An adaptive mask for discriminating between ice-free pixels and snow-free pixels is also applied. The resulting database is thus a new consistent hourly series of near-surface air temperatures during the summer (without snow). The mean accuracy is on the order of 2.5–3 K when compared with the synchronous in situ air temperature and different gridded datasets over Canada and Alaska. The trend over the last 10 yr confirms observed climate evolution: an increase in summer surface temperature of +0.09° ± 0.04°C yr−1, at the 90% confidence level, for Canada between 1992 and 2002, whereas a decrease of −0.15° ± 0.05°C yr−1, at the 95% confidence level, is observed for Alaska. Spatial and temporal anomalies show regional impacts of meteorological phenomena such as the El Niño extreme warm summer episode of 1998, the decrease in temperatures in 1992 in Canada following the volcanic eruption of Mount Pinatubo in June 1991, and the strong drought in the prairies in 2001. The annual sum of positive degree-days (thawing index) has been related to the permafrost distribution. The lower values of the derived thawing index (&lt;1400 degree-days) are related well to the presence of continuous and dense discontinuous permafrost. The observed increase in the thawing index during the 1992–2002 period represents a decrease of classified permafrost area of 7%.

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

2005 ◽  
Vol 22 (7) ◽  
pp. 909-929 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Christian D. Kummerow
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Dimensional Structure ◽  
Retrieval Algorithm ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

scholarly journals Fundamental Climate Data Records of Microwave Brightness Temperatures

2018 ◽  
Vol 10 (8) ◽  
pp. 1306 ◽  
Author(s):  
Wesley Berg ◽  
Rachael Kroodsma ◽  
Christian Kummerow ◽  
Darren McKague
Keyword(s):  
Land Surface ◽  
Surface Wind ◽  
Precipitable Water ◽  
Original Data ◽  
Climate Data ◽  
Sea Ice Extent ◽  
Brightness Temperatures ◽  
Precipitation Total ◽  
Data Record ◽  
Microwave Imager

An intercalibrated Fundamental Climate Data Record (FCDR) of brightness temperatures (Tb) has been developed using data from a total of 14 research and operational conical-scanning microwave imagers. This dataset provides a consistent 30+ year data record of global observations that is well suited for retrieving estimates of precipitation, total precipitable water, cloud liquid water, ocean surface wind speed, sea ice extent and concentration, snow cover, soil moisture, and land surface emissivity. An initial FCDR was developed for a series of ten Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments on board the Defense Meteorological Satellite Program spacecraft. An updated version of this dataset, including additional NASA and Japanese sensors, has been developed as part of the Global Precipitation Measurement (GPM) mission. The FCDR development efforts involved quality control of the original data, geolocation corrections, calibration corrections to account for cross-track and time-dependent calibration errors, and intercalibration to ensure consistency with the calibration reference. Both the initial SSMI(S) and subsequent GPM Level 1C FCDR datasets are documented, updated in near real-time, and publicly distributed.

scholarly journals Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

2015 ◽  
Vol 12 (8) ◽  
pp. 7665-7687 ◽  
Author(s):  
C. L. Pérez Díaz ◽  
T. Lakhankar ◽  
P. Romanov ◽  
J. Muñoz ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Balance Model ◽  
Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

Sign in / Sign up

Export Citation Format

Share Document