Dan Tarpley

Recalibration of microwave sounding unit for climate studies using simultaneous nadir overpasses

[1] The measurements from microwave sounding unit (MSU) on board different NOAA polar-orbiting satellites have been extensively used for detecting atmospheric temperature trend during the last several decades. However, temperature trends derived from these measurements are under significant debate, mostly caused by calibration errors. This study recalibrates the MSU channel 2 observations at level 0 using the postlaunch simultaneous nadir overpass (SNO) matchups and then provides a well-merged new MSU 1b data set for climate studies. The calibration algorithm consists of a dominant linear response of the MSU raw counts to the Earth-view radiance plus a smaller quadratic term. Uncertainties are represented by a constant offset and errors in the coefficient for the nonlinear quadratic term. A SNO matchup data set for nadir pixels with criteria of simultaneity of less than 100 s and within a ground distance of 111 km is generated for all overlaps of NOAA satellites. The simultaneous nature of these matchups eliminates the impact of orbital drifts on the calibration. A radiance error model for the SNO pairs is developed and then used to determine the offsets and nonlinear coefficients through regressions of the SNO matchups. It is found that the SNO matchups can accurately determine the differences of the offsets as well as the nonlinear coefficients between satellite pairs, thus providing a strong constraint to link calibration coefficients of different satellites together. However, SNO matchups alone cannot determine the absolute values of the coefficients because there is a high degree of colinearity between satellite SNO observations. Absolute values of calibration coefficients are obtained through sensitivity experiments, in which the percentage of variance in the brightness temperature difference time series that can be explained by the warm target temperatures of overlapping satellites is a function of the calibration coefficient. By minimizing these percentages of variance for overlapping observations, a new set of calibration coefficients is obtained from the SNO regressions. These new coefficients are significantly different from the prelaunch calibration values, but they result in bias-free SNO matchups and near-zero contaminations by the warm target temperatures in terms of the calibrated brightness temperature. Applying the new calibration coefficients to the Level 0 MSU observations, a well-merged MSU pentad data set is generated for climate trend studies. To avoid errors caused by small SNO samplings between NOAA 10 and 9, observations only from and after NOAA 10 are used. In addition, only ocean averages are investigated so that diurnal cycle effect can be ignored. The global ocean-averaged intersatellite biases for the pentad data set are between 0.05 and 0.1 K, which is an order of magnitude smaller than that obtained when using the unadjusted calibration algorithm. The ocean-only anomaly trend for the combined MSU channel 2 brightness temperature is found to be 0.198 K decade -1 during 1987-2003.

The Enhanced NOAA Global Land Dataset from the Advanced Very High Resolution Radiometer

Abstract Global mapped data of reflected radiation in the visible (0.63 μm) and near-infrared (0.85 μm) wavebands of the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration satellites have been collected as the global vegetation index (GVI) dataset since 1982. Its primary objective has been vegetation studies (hence its title) using the normalized difference vegetation index (NDVI) calculated from the visible and near-IR data. The second-generation GVI, which started in April 1985, has also included brightness temperatures in the thermal IR (11 and 12,um) and the associated observation-illumination geometry. This multiyear, multispectral, multisatellite dataset is a unique tool for global land studies. At the same time, it raises challenging remote sensing and data management problems with respect to uniformity in time, enhancement of signal-to-noise ratio, retrieval of geophysical parameters from satellite radiances, and large data volumes. The authors...

Evaluation of the Relationship between Air and Land Surface Temperature under Clear- and Cloudy-Sky Conditions

Abstract Clear and cloudy daytime comparisons of land surface temperature (LST) and air temperature (Tair) were made for 14 stations included in the U.S. Climate Reference Network (USCRN) of stations from observations made from 2003 through 2008. Generally, LST was greater than Tair for both the clear and cloudy conditions; however, the differences between LST and Tair were significantly less for the cloudy-sky conditions. In addition, the relationships between LST and Tair displayed less variability under the cloudy-sky conditions than under clear-sky conditions. Wind speed, time of the observation of Tair and LST, season, the occurrence of precipitation at the time of observation, and normalized difference vegetation index values were all considered in the evaluation of the relationship between Tair and LST. Mean differences between LST and Tair of less than 2°C were observed under cloudy conditions for the stations, as compared with a minimum difference of greater than 2°C (and as great as 7+°C) for th...

Developing Algorithm for Operational GOES-R Land Surface Temperature Product

The Geostationary Operational Environmental Satellite (GOES) program is developing the Advanced Baseline Imager (ABI), a new generation sensor to be carried onboard the GEOS-R satellite (launch expected in 2014). Compared to the current GOES Imager, ABI will have significant advantages for retrieving land surface temperature (LST) as well as providing qualitative and quantitative data for a wide range of applications. The infrared bands of the ABI sensor are designed to achieve a spatial resolution of 2 km at nadir and a noise equivalent temperature of 0.1 K. These improve the imager specifications and compare well with those of polar-orbiting sensors (e.g., Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer). In this paper, we discuss the development of a split window LST algorithm for the ABI sensor. First, we simulated ABI sensor data using the MODTRAN radiative transfer model and NOAA88 atmospheric profiles. To model land conditions, we developed emissivity data for 78 virtual surface types using the surface emissivity library from Snyder Using the simulation results, we performed regression analyses with the candidate LST algorithms. Algorithm coefficients were stratified for dry and moist atmospheres as well as for daytime and nighttime conditions. We estimated the accuracy and sensitivity of each algorithm for different sun-view geometries, emissivity errors, and atmospheric assessments. Finally, we evaluated the most promising algorithm using real data from the GOES-8 Imager and SURFace RADiation Network. The results indicate that the optimized LST algorithm meets the required accuracy (2.3 K) of the GOES-R mission.

Sensitivity of the NCEP/Noah land surface model to the MODIS green vegetation fraction data set

[1] Land surface processes are strongly controlled by vegetation cover. Current land surface models represent vegetation as a combination of leaf area index (LAI) and green vegetation fraction (GVF) parameters. The purpose of the study is to examine the impact of a spatially and temporally detailed Moderate Resolution Imaging Spectroradiometer (MODIS)-based GVF on surface processes in the NCEP Noah land surface model. The largest differences between the GVF data set currently used by the Noah model and the new MODIS GVF data set occur in winter and for tree-dominated vegetation classes. The greatest impact of the new GVF data on the surface energy and water balance is seen during the summer, when the transpiration is increased by more than 10 W/m 2 on average for most vegetation types and the July averaged daily transpiration rate is increased by up to 50 W/m 2 for evergreen needleleaf sites.

Validation of GOES-R Satellite Land Surface Temperature Algorithm Using SURFRAD Ground Measurements and Statistical Estimates of Error Properties

Validation of satellite land surface temperature (LST) is a challenge because of spectral, spatial, and temporal variabilities of land surface emissivity. Highly accurate in situ LST measurements are required for validating satellite LST products but are very hard to obtain, except at discrete points or for very short time periods (e.g., during field campaigns). To compare these field-measured point data with moderate-resolution (1 km) satellite products requires a scaling process that can introduce errors that ultimately exceed those in the satellite-derived LST products whose validation is sought. This paper presents a new method of validating the Geostationary Operational Environmental Satellite (GOES) R-Series (GOES-R) Advanced Baseline Imager (ABI) LST algorithm. It considers the error structures of both ground and satellite data sets. The method applies a linear fitting model to the satellite data and coregistered “match-up” ground data for estimating the precisions of both data sets. In this paper, GOES-8 Imager data were used as a proxy of the GOES-R ABI data for the satellite LST derivation. The in situ data set was obtained from the National Oceanic and Atmospheric Administrations SURFace RADiation (SURFRAD) budget network using a stringent match-up process. The data cover one year of GOES-8 Imager observations over six SURFRAD sites. For each site, more than 1000 cloud-free match-up data pairs were obtained for day and night to ensure statistical significance. The average precision over all six sites was found to be 1.58 K, as compared to the GOES-R LST required precision of 2.3 K. The least precise comparison at an individual SURFRAD site was 1.8 K. The conclusion is that, for these ground truth sites, the GOES-R LST algorithm meets the specifications and that an upper boundary on the precision of the satellite LSTs can be determined.

Albedo of the U.S. Great Plains as Determined from NOAA-9 AVHRR Data

Abstract The seasonal variation of surface albedo is derived from NOAA-9 AVHRR observations of the US. Great Plains during the snow-free months of 1986 and 1987. Monthly albedo maps are constructed using a simple model-independent technique which includes recording of the cloud-free data by 9-day satellite repeat cycle, spatial interpolation of the data averaging on each day of the cycle, and averaging over the cycle to obtain a monthly mean estimate. The surface albedo is obtained by applying a narrowband-to-broadband conversion and a simple atmospheric correction to the clear-sky albodo of each mouth. The phenological changes of four target areas (two forest lands and two croplands) are analyzed. In some areas, crop development can double the albedo values within the growing season. Such changes are important in radiation budget calculations. On the whole, the results are in good agreement with tabulated values based on albedos reported in the literature for various surface types.

Evaluation of GOES-R Land Surface Temperature Algorithm Using SEVIRI Satellite Retrievals With In Situ Measurements

Validation of the land surface temperature (LST) algorithm and product is a challenging task for future Geostationary Operational Environmental Satellite R-Series (GOES-R) applications. Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) full-disk data have been used as the key proxy data for the GOES-R LST algorithm and product development. A split window algorithm developed to generate GOES-R LST was applied to MSG SEVIRI data with the algorithm coefficients adjusted to the specific SEVIRI bands. The retrieved LST values were evaluated with in situ LST obtained from four validation stations with different surface features over various time periods. The results presented here clearly highlight the importance of accurate and seasonally representative site characterizations for the LST validation process. Furthermore, the study gives valuable insights into the limitations of the current version of the LST retrieval algorithm and on how to further refine it for the next generation of satellite sensors.

Using SURFRAD to Verify the NOAA Single-Channel Land Surface Temperature Algorithm

AbstractBecause of spectral shifts from instrument to instrument in the operational NOAA satellite imager longwave infrared channels, the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) has developed a single-channel land surface temperature (LST) algorithm based on the observed 11-μm radiances, numerical weather prediction data, and radiative transfer modeling that allows for consistent results from the Geostationary Operational Environmental Satellite-I/L (GOES-I/L), GOES-M–P, and Advanced Very High Resolution Radiometer (AVHRR)/1 through 3 sensor versions. This approach is implemented in the real-time NESDIS processing systems [GOES Surface and Insolation Products (GSIP) and Clouds from AVHRR Extended (CLAVR-x)], and in the Pathfinder Atmospheres–Extended (PATMOS-x) climate dataset. An analysis of the PATMOS-x LST against that derived from the upwelling broadband longwave flux at each Surface Radiation Network (SURFRAD) site showed that biases in PATMOS-x were approximatel...

Characterization of variability at in situ locations for calibration/validation of satellite-derived land surface temperature data

Calibration and validation (cal/val) of data derived from satellite-based instruments is critical to providing accurate global measurements of environmental variables at useful spatial and temporal resolutions. In this letter, statistical models based on linear regressions employing various predictor variables were utilized to elucidate appropriate methods of characterizing variability near ground sites that might be used for calibration and validation. Regressions based on more complex statistics performed no better than those based on easily derived statistics, and the regression relations provided valuable information for assessing the potential quality of satellite-based measures of land surface temperature.