M. K. Rama Varma Raja

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.

Diurnal and Scan Angle Variations in the Calibration of GOES Imager Infrared Channels

The current Geostationary Operational Environmental Satellite (GOES) Imager infrared (IR) channels experience a midnight effect that can result in erroneous instrument responsivity around satellite midnight. An empirical method named the Midnight Blackbody Calibration Correction (MBCC) was developed and implemented in the GOES Imager IR operational calibration, aiming to correct the midnight calibration errors. The main objective of this study is to evaluate the MBCC performance for the GOES-11/-12 Imager IR channels by examining the diurnal variation of the mean brightness temperature (Tb) bias with respect to reference instruments. Two well-calibrated hyperspectral radiometers on low Earth orbits (LEOs), the Atmospheric Infrared Sounder on the Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) on the Metop-A satellite, are used as the reference instruments in this study. However, as the timing of the collocated geostationary-LEO intercalibration data is related to the GOES scan angle, it is then necessary to assess the GOES scan angle calibration variations, which becomes the second objective of this study. Our results show that the applications and performance of the MBCC method varies greatly between the different channels and different times. While it is usually applied with high frequency for about 8 h around satellite midnight for the short-wave channels (Ch2), it may only be intensively used right after satellite midnight or even barely used for the other IR channels. The MBCC method, if applied with high frequency, can reduce the mean day/night calibration difference to less than 0.15 K in almost all the GOES IR channels studied in this paper except for Ch4 (10.7 μm). The uncertainty of the nighttime GOES and IASI Tb difference for different scan angles is less than 0.1 K in each IR channel, indicating that there is no apparent systematic variation with the scan angle, and therefore, the estimated diurnal cycles of GOES Imager calibration is not prone to the systematic effects due to scan angle.

Evaluation of the Sensor Data Record from the nadir instruments of the Ozone Mapping Profiler Suite (OMPS)

This paper evaluates the first 15 months of the Ozone Mapping and Profiler Suite (OMPS) Sensor Data Record (SDR) acquired by the nadir sensors and processed by the National Oceanic and Atmospheric Administration Interface Data Processing Segment. The evaluation consists of an inter-comparison with a similar satellite instrument, an analysis using a radiative transfer model, and an assessment of product stability. This is in addition to the evaluation of sensor calibration and the Environment Data Record product that are also reported in this Special Issue. All these are parts of synergetic effort to provide comprehensive assessment at every level of the products to ensure its quality. It is found that the OMPS nadir SDR quality is satisfactory for the current Provisional maturity. Methods used in the evaluation are being further refined, developed, and expanded, in collaboration with international community through the Global Space-based Inter-Calibration System, to support the upcoming long-term monitoring.

The evolution of the performance of the AVHRR, HIRS and AMSU-A instruments on-board Metop-A after one year in-orbit

It is well known that the varying geometrical relationships between the Sun and the Earth throughout the year affect to some degree the performance of the instruments onboard Earth orbiting satellites. Following the commissioning of MetOp-A, EUMETSAT and NOAA have continued monitoring the in-orbit performance of AVHRR, HIRS and AMSU-A. The data acquired since the launch of the satellite has allowed studying how the yearly seasonal variations affect the instrument performance.

Algorithm development for land surface temperature measurement from GOES-R satellite

The Geostationary Operational Environmental Satellite (GOES) program is developing a new generation sensor, the Advanced Baseline Imager (ABI), to be carried on the GOES-R satellite to be lunched in approximately in 2014. Compared to the current GOES imager, ABI will have significant advantages for measuring land surface temperature as well as to providing qualitative and quantitative data for a wide range of applications. Specifically, spatial resolution of the ABI sensor is 2 km, and the infrared window noise equivalent temperature is 0.1 K, which are very close to the polarorbiting satellite sensors such as AVHRR. Most importantly, ABI observes the full disk every five minutes, which not only provides more cloud-free measurements but also makes daily temperature variation analysis possible. In this study we developed split window algorithms for the LST measurement from the ABI sensor. We generated the ABI sensor data using MODTRAN radiative transfer model and NOAA88 atmospheric profiles and ran regression analyses for the LST algorithm development. The algorithms are developed by optimizing existing split window LST algorithms and adding a path length correction term to minimize the retrieval errors due to difference atmospheric path absorption from nadir view to the edge-of-scan. The algorithm coefficients are stratified for dry and moist atmospheric conditions, as well as for the daytime and nighttime. The algorithm sensitivity to land surface emissivity uncertainty is analyzed to ensure the algorithm performance.

Intercomparison of Doppler Wind Lidar Velocity Measurements

Three collocated Doppler Wind Lidars (DWLs) were used to measure winds during a demonstration campaign in New Hampshire September 2000. Corresponding line-of-sight (LOS) velocities determined by pairs of DWLs realized good agreement under clear sky conditions with random differences that increase as a function of decreasing signal strength. The radiosonde wind component projected along the DWL LOS typically showed good agreement. Clouds caused sharp attenuation of the signal and adversely affected LOS retrievals at cloud levels and beyond. Implications of the results for improving the instruments and organizing future Doppler wind lidar field campaigns are discussed.