Alan Basist

An Eight-Year (1987–1994) Time Series of Rainfall, Clouds, Water Vapor, Snow Cover, and Sea Ice Derived from SSM/I Measurements

Abstract The Special Sensor Microwave/Imager (SSM/I), first placed into operation in July 1987, has been making measurements of earth-emitted radiation for over eight years. These data are used to estimate both atmospheric and surface hydrological parameters and to generate a time series of global monthly mean products averaged to a 1° lat × 1° long grid. Specifically, this includes monthly estimates of rainfall and its frequency, cloud liquid water and cloud frequency, water vapor, snow cover frequency, and sea ice frequency. This study uses seasonal mean values to demonstrate the spatial and temporal distributions of these hydrological variables. Examples of interannual variability such as the 1993 flooding in the Mississippi Valley and the 1992–93 snow cover changes over the United States are used to demonstrate the utility of these data for regional applications.

Global identification of snowcover using SSM/I measurements

Visible satellite sensors have monitored snowcover throughout the Northern Hemisphere for almost thirty years. These sensors can detect snowcover during daylight, cloud-free conditions. The operational procedure developed by NOAA/NESDIS requires an analyst to manually view the images in order to subjectively distinguish between clouds and snowcover. Because this procedure is manually intensive, it is only performed weekly. Since microwave sensors see through nonprecipitating clouds, snowcover can be determined objectively without the intervention of an analyst. Furthermore, microwave sensors can provide daily analysis of snowcover in real-time, which is essential for operational forecast models and regional hydrologic monitoring. Snowcover measurements are obtained from the Special Sensor Microwave Imager (SSM/I), flown aboard the DMSP satellites. A decision tree, containing various filters, is used to separate the scattering signature of snowcover from other scattering signatures. Problem areas are discussed and when possible, a filter is developed to eliminate biases. The finalized decision tree is an objective algorithm to monitor the global distribution of snowcover. Comparisons are made between the SSM/I snowcover product and the NOAA/NESDIS subjectively analyzed weekly product.

Statistical Relationships between Topography and Precipitation Patterns

Abstract Statistical relationships between topography and the spatial distribution of mean annual precipitation are developed for ten distinct mountainous regions. These relationships are derived through linear bivariate and multivariate analyses, using six topographic variables as predictors of precipitation. These predictors are elevation, slope, orientation, exposure, the product (or interaction) of slope and orientation, and the product of elevation and exposure. The two interactive terms are the best overall bivariate predictors of mean annual precipitation, whereas orientation and exposure are the strongest noninteractive bivariate predictors. The regression equations in many of the climatically similar regions tend to have similar slope coefficients and similar y-intercept values, indicating that local climatic conditions strongly influence the relationship between topography and the spatial distribution of precipitation. In contrast, the regression equations for the tropical and extratropical regi...

Using the Special Sensor Microwave/Imager to Monitor Land Surface Temperatures, Wetness, and Snow Cover

Abstract 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 f...

Comparison of Tropospheric Temperatures Derived from the NCEP/NCAR Reanalysis, NCEP Operational Analysis, and the Microwave Sounding Unit

Abstract 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 abo...

Cloud Liquid Water Climatology from the Special Sensor Microwave/Imager

Abstract A Special Sensor Microwave/Imager (SSM/I) algorithm is developed to measure both cloud liquid water path (LWP) and cloud frequency (CF) over the oceans. For climate analysis, the LWP and CF parameters are computed on pentad and monthly timescales. Comparisons are made between cloud frequencies obtained from microwave and visible/infrared measurements. It is shown that the SSM/I CF correlates with International Satellite Cloud Climatology Program low- and middle-level cloudiness. Interannual variations of monthly LWP are found to be strongly correlated with El Nino and La Nina events. In general, positive LWP anomalies are associated with positive SST anomalies. However, positive LWP anomalies may also occur in regions of negative SST anomalies. This is probably due to an increase in warm top rain clouds, produced from low-level convergence. When pentads of outgoing longwave radiation data are compared to the LWP, they both show the detailed structure for atmospheric intraseasonal oscillations at ...

Using the Special Sensor Microwave Imager to Monitor Surface Wetness

The frequencies flown on the Special Sensor Microwave Imager (SSM/I) are sensitive to liquid water near the earth’s surface. These frequencies are primarily atmospheric window channels, which receive the majority of their radiation from the surface. Liquid water near the surface depresses the emissivity as a function of wavelength. The relationship between brightness temperatures at different frequencies is used to dynamically derive the amount of liquid water in each SSM/I observation at 1/38 resolution. These data are averaged at 18 resolution throughout the globe for each month during the period of 1992‐97, and the 6-yr monthly means and the monthly anomalies of the wetness index are computed from this base period. To quantify the relationship between precipitation and surface wetness, these anomalies are compared with precipitation anomalies derived from the Global Precipitation Climate Program. The analysis was performed for six agricultural regions across six continents. There is generally a good correspondence between the two variables. The correlation generally increases when the wetness index is compared with precipitation anomalies accumulated over a 2-month period. These results indicate that the wetness index has a strong correspondence to the upper layer of the soil moisture in many cultivated areas of the world. The region in southeastern Australia had the best relationship, with a correlation coefficient of 0.76. The Sahel, France, and Argentina showed that the wetness index had memory of precipitation anomalies from the previous months. The memory is shorter for southeastern Australia and central China. The weakest correlations occurred over the southeastern United States, where the surface is covered by dense vegetation. The unique signal, strengths, and weaknesses of the wetness index in each of the six study regions are discussed.

A Comparison between Snow Cover Products Derived from Visible and Microwave Satellite Observation

Abstract A comparison between two satellite-derived snow cover products demonstrates the strengths and weakness of each procedure. The current NESDIS operational product is subjectively derived from visible satellite imagery. The analysis is performed once a week, using the most recent clear view of the surface. The experimental product is objectively derived from daily microwave measurements observed by polar-orbiting satellites. The operational product uses a high albedo in the visible spectrum to identify snow cover, whereas the experimental product uses a high albedo in the visible spectrum to identify snow cover, whereas the experimental product uses a passive microwave scattering signature. Comparisons between the operational and experimental product show good agreement in the extent and distribution of snow cover during the middle of the winter and summer seasons. However, the agreement weakness in the transition seasons and along the southern edge of the snowpack. The analysis suggests that the op...

An operational near‐real‐time global temperature index

To capture the global land surface temperature signal in a timely way, a blend of traditional long-term in situ climatic data sets, combined with real time Global Telecommunications System monthly CLIMAT summaries is employed. For the global sea surface, long-term ship data climatologies are combined with a blend of ship, buoy, and satellite data to provide the greatest possible coverage over the oceans. The result is a global century-scale surface temperature index that closely parallels other widely published global surface temperature measurements and can be updated monthly a week or two after the end of a month.

Calibration and Verification of Land Surface Temperature Anomalies Derived from the SSM/I

Abstract 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...