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Alan N. Basist and Muthuvel Chelliah

The Climate Prediction Center has used atmospheric temperatures for data analysis from the National Centers for Environmental Prediction (NCEP) model since 1979. Unfortunately, model changes have adversely affected the stability of the climatologic fields, introducing time-varying biases in the anomaly patterns of the Climate Diagnostic Data Base (CDDB). Fortunately, NCEP has addressed this issue by rerunning a state-of-the-art model using fixed assimilation, parameterization, and physics in order to derive a true climatology and anomalies. The authors compare the previous CDDB temperatures with those derived from the stable reanalysis. Results show major improvements for climate diagnostics and monitoring. Also compared are the reanalysis temperatures with brightness temperature Tb observed by the Microwave Sounding Units (MSU), flown aboard the National Oceanic and Atmospheric Administration (NOAA) series of polar-orbiting satellites (TIROS-N to NOAA-14). This MSU dataset has a precision of about 0.02°C globally, and it is available from December 1978. Therefore, the 17 levels of the reanalysis level temperature were weighted to simulate the MSU Tb in order to measure its precision over the 17-yr record. Global time series of the spatial correlations between full fields approach 1.0 throughout the entire record, whereas correlations for the anomaly fields can drop below 0.8 during the high sun season in the Northern Hemisphere. In 1994 the correlations drop below 0.65, which is the largest difference between the two datasets. An EOF on the global Tb differences from both datasets identified a relative drift beginning in 1991. The maximum loading was in the tropical Pacific, although it also extended over the tropical Indian Ocean and the Asian landmass. Results indicate that the reanalysis anomalies are getting progressively colder, relative to the MSU, during the early 1990s. The authors associate this drift with the changes in satellite retrievals and a reduction of Soviet Union data during its breakup. Additional sources of bias may be associated with aerosol contamination after the Mt. Pinotuba eruption and/or drift in the NOAA-11 sensor. Although there is a relative offset in the anomalies, the reanalysis temperatures have a better correspondence with the radiosonde network after 1990. Therefore it appears that the bias is associated with an improvement in the reanalysis input data during the last several years. Since changes in the datasets assimilated into the model can introduce a slight bias, new procedures should be developed to minimizes these effects in any future reanalysis. Finally, although the reanalysis has a slight drift in the later years, the comparison with the MSU spatial anomalies generally showed excellent results. The reanalysis represents a substantial improvement over the CDDB for monitoring climate variability.

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Gerald D. Bell and Alan N. Basist

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Claude N. Williams, Alan Basist, Thomas C. Peterson, and Norman Grody

The current network of internationally exchanged in situ station data is not distributed evenly nor densely around the globe. Consequently, the in situ data contain insufficient information to identify fine spatial structure and variations over many areas of the world. Therefore, satellite observations need to be blended with in situ data to obtain higher resolution over the global land surface. Toward this end, the authors calibrated and independently verified an algorithm that derives land surface temperatures from the Special Sensor Microwave/Imager (SSM/I). This study explains the technique used to refine a set of equations that identify various surface types and to make corresponding dynamic emissivity adjustments. This allowed estimation of the shelter height temperatures from the seven channel measurements flown on the SSM/I instrument. Data from first-order in situ stations over the eastern half of the United States were used for calibration and intersatellite adjustment. The results show that the observational difference between the in situ point measurements and the SSM/I-derived areal values is about 2°C with statistical characteristics largely independent of surface type. High-resolution monthly mean anomalies generated from the U.S. cooperative network served as independent verification over the same study area. This verification work determined that the standard deviation of the monthly mean anomalies is 0.76°C at each 1° × 1° grid box. This level of accuracy is adequate to blend the SSM/I-derived temperature anomaly data with in situ data for monitoring global temperature anomalies in finer detail.

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Alan N. Basist, Chester F. Ropelewski, and Norman C. Grody

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The Microwave Sounding Units (MSU) aboard the NOAA series of polar-orbiting satellites (TIROS-N to NOAA-12) have provided stable and precise measurements of vertically integrated atmospheric temperature since December 1978. Comparisons are made between the MSU channel measurements and temperatures derived from the global data assimilation system (GDAS) at the National Meteorological Center (NMC) for the period 1979–1990. The largest correlations occur at high to midlatitudes, where the troposphere exhibits large monthly anomaly fields, and where radiosondes provide ample coverage for the GDAS. Intermonthly differences from each dataset had global correlations above 0.97. However, poor correlations with MSU were noted over areas of high terrain and tropical landmasses. These poorer correlations can be attributed to temporal changes and data limitations in the GDAS analysis. Comparisons between the GDAS and MSU temperature anomaly fields indicate that frequent model changes mask the climate signal in the GDAS analysis. Nonetheless, the study suggests that both GDAS- and MSU-derived temperature anomalies detect similar spatial and temporal variability over regions where the GDAS is data rich and the signal is large, that is, the El Niño-Southern Oscillations. This study suggests that the NMC reanalysis, using a fixed assimilation model, will produce a stable dataset of tropospheric temperatures. Therefore, the 35 years of reanalyzed NMC model data can he used in conjunction with satellite data to improve the suite of tools used in climate monitoring.

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Thomas C. Peterson, Alan N. Basist, Claude N. Williams, and Norman C. Grody

A near-global surface temperature dataset was produced by blending several sources of information. For the oceans, these include in situ and infrared satellite-derived sea surface temperatures that were already processed into a monthly product. Land data analysis uses two sources of data. The first is high quality monthly in situ reports from the Global Historical Climatologic Network with more than 1000 stations from around the world. The second source of information is the recently developed passive microwave satellite-derived land surface temperature derivation methodology described in Williams et al. These data are blended on a 1° × 1° grid that excludes only ice- and snow-covered regions lacking in situ observations. Available starting in January 1992 and updated 10 days after the end of the calendar month, this product is useful for monitoring regional climate anomalies and provides insights into climate variations.

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Alan Basist, Norman C. Grody, Thomas C. Peterson, and Claude N. Williams

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The worldwide network of in situ land surface temperatures archived in near-real time at the National Climatic Data Center (NCDC) has limited applications, since many areas are poorly represented or provide no observations. Satellite measurements offer a possible way to fill in the data voids and obtain a complete map of surface temperature over the entire globe. A method has been developed to calculate near-surface temperature using measurements from the Special Sensor Microwave/Imager (SSM/I). To accomplish this, the authors identify numerous surface types and make dynamic adjustments for variations in emissivity. Training datasets were used to define the relationship between the seven SSM/I channels and the near-surface temperature. For instance, liquid water on the surface reduces emissivity; therefore, the authors developed an adjustment to correct for this reduction. Other surface types (e.g., snow, ice, and deserts) as well as precipitation are identified, and numerous adjustments and/or filters were developed for these features. The article presents the results obtained from training datasets, as well as an independent case study, containing extreme conditions for deriving temperature from the SSM/I. The U.S. networks of first-order and cooperative stations, quality controlled by NCDC, serve as validation data. The correlation between satellite-derived and in situ temperatures during the independent case (“Blizzard of 1996”) was greater than 0.95, and the standard error was 2°C. The authors also present SSM/I-derived snow cover and wetness maps from this 2-week period of the blizzard. A prototype for blending the satellite and in situ measurements into a single land surface temperature product is also presented.

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