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- Author or Editor: Michael R. Howland x
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Abstract
Moisture budgets are calculated for premonsoon and monsoon onset conditions in the northeastern Arabian Sea during summer 1979 from kinematic analysis of aircraft dropsonde, ship and island radiosonde, and satellite-derived winds. Dramatic changes are observed between the premonsoon and monsoon onset mean kinematic and moisture fields. Specific humidity increased as much as 5 g kg−1 in much of the middle troposphere between 29 May and 17 June 1979. This is apparently due to deep convection during the monsoon onset period and mid-level advection of moisture during the premonsoon period. Flux of moisture through the budget boundaries is comparable to previous estimates for the Arabian Sea. It is shown that the loss of moisture through cirrus outflow accounts for only 1–3% of the total budget flux. Evaporation from the sea surface is 3 to 4 times higher during the onset period and was greatest south of 12°N. Maps of precipitation as a residual of the moisture budget computations agree remarkably well with convective features seen in satellite imagery. During the monsoon onset period, rainfall averaged about 1 mm h−1 over the entire budget area. In order to test the validity of the combined data base and moisture budget computation two independent estimates of precipitation were made using a Krishnamurti et al. parameterization scheme and the Stout et al. satellite technique. Both showed good agreement to the budget results.
Abstract
Moisture budgets are calculated for premonsoon and monsoon onset conditions in the northeastern Arabian Sea during summer 1979 from kinematic analysis of aircraft dropsonde, ship and island radiosonde, and satellite-derived winds. Dramatic changes are observed between the premonsoon and monsoon onset mean kinematic and moisture fields. Specific humidity increased as much as 5 g kg−1 in much of the middle troposphere between 29 May and 17 June 1979. This is apparently due to deep convection during the monsoon onset period and mid-level advection of moisture during the premonsoon period. Flux of moisture through the budget boundaries is comparable to previous estimates for the Arabian Sea. It is shown that the loss of moisture through cirrus outflow accounts for only 1–3% of the total budget flux. Evaporation from the sea surface is 3 to 4 times higher during the onset period and was greatest south of 12°N. Maps of precipitation as a residual of the moisture budget computations agree remarkably well with convective features seen in satellite imagery. During the monsoon onset period, rainfall averaged about 1 mm h−1 over the entire budget area. In order to test the validity of the combined data base and moisture budget computation two independent estimates of precipitation were made using a Krishnamurti et al. parameterization scheme and the Stout et al. satellite technique. Both showed good agreement to the budget results.
Abstract
A new technique is described for estimating daily rainfall by means of visible and infrared geostationary satellite imagery. It is designed for the tropics and warm-season midlatitudes. Because it operates on a grid of points and measures time changes at these points, the technique has been named “grid history.”
It is assumed that at any grid point in some image belonging to a sequence, by means of spectral, textural and evolutionary information, it is possible to classify instantaneous rain rate as nil, light, moderate or heavy. Then the total rainfall over a day is the sum over three classes of the product of frequency and class average rate.
The class average rates have been determined by least-squares multivariate linear regression of frequencies on observed rainfalls. The areas treated are South China Sea, India, Arabian Sea, tropical North Atlantic Ocean and Amazonia. Inland India had the lowest (driest) class average rates (coefficients), coastal India the largest (wettest) coefficients. Differences in coefficients were least for the Arabian Sea and Atlantic Ocean. There the class average rates were roughly zero (by definition), 1.5, 6 and 15 mm h−1. For the strongly convective rain regimes treated here, it was found to be important to “look” at the area at least once per hour. A loss of accuracy in estimates over land apparently was due to unexpectedly large terrain and synoptic effects. Best-circumstance estimates of daily rainfall for an area 100 km on a side should be within a factor of 2 of true rainfall.
Abstract
A new technique is described for estimating daily rainfall by means of visible and infrared geostationary satellite imagery. It is designed for the tropics and warm-season midlatitudes. Because it operates on a grid of points and measures time changes at these points, the technique has been named “grid history.”
It is assumed that at any grid point in some image belonging to a sequence, by means of spectral, textural and evolutionary information, it is possible to classify instantaneous rain rate as nil, light, moderate or heavy. Then the total rainfall over a day is the sum over three classes of the product of frequency and class average rate.
The class average rates have been determined by least-squares multivariate linear regression of frequencies on observed rainfalls. The areas treated are South China Sea, India, Arabian Sea, tropical North Atlantic Ocean and Amazonia. Inland India had the lowest (driest) class average rates (coefficients), coastal India the largest (wettest) coefficients. Differences in coefficients were least for the Arabian Sea and Atlantic Ocean. There the class average rates were roughly zero (by definition), 1.5, 6 and 15 mm h−1. For the strongly convective rain regimes treated here, it was found to be important to “look” at the area at least once per hour. A loss of accuracy in estimates over land apparently was due to unexpectedly large terrain and synoptic effects. Best-circumstance estimates of daily rainfall for an area 100 km on a side should be within a factor of 2 of true rainfall.
Abstract
In an attempt to determine the feasibility of detecting and monitoring severe weather with future satellite passive microwave observations, the severe weather characteristics of convective storms as observed by the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) are investigated. Low 37 GHz brightness temperatures (due to scattering of upwelling radiation by precipitation size ice) were related to the occurrence of severe weather (large hail, strong winds or wind damage, tornados and funnel clouds) within one hour of the satellite observation time. During 1979 and 1980 over the study area within the United States, there were 263 storms that had cold 37 GHz signatures. Of these storms, 15 percent were reported as severe. The relative number of storms falling in hail, wind, or tornadic categories did not differ from those expected climatologically. Critical Success Indices (CSIs) of 0.32, 0.48 and 0.38 were achieved for the low brightness temperature thresholding of severe versus nonsevere storms during 1979, 1980 and the two years combined, respectively. The preliminary indication is that a future geostationary passive microwave imaging capability at 37 GHz (or possibly higher frequencies), with sufficient spatial and temporal resolution, would facilitate the detection and monitoring of severe convective storms. This capability would provide a useful complement to radar, especially over most of the globe which is not covered by radar.
Abstract
In an attempt to determine the feasibility of detecting and monitoring severe weather with future satellite passive microwave observations, the severe weather characteristics of convective storms as observed by the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) are investigated. Low 37 GHz brightness temperatures (due to scattering of upwelling radiation by precipitation size ice) were related to the occurrence of severe weather (large hail, strong winds or wind damage, tornados and funnel clouds) within one hour of the satellite observation time. During 1979 and 1980 over the study area within the United States, there were 263 storms that had cold 37 GHz signatures. Of these storms, 15 percent were reported as severe. The relative number of storms falling in hail, wind, or tornadic categories did not differ from those expected climatologically. Critical Success Indices (CSIs) of 0.32, 0.48 and 0.38 were achieved for the low brightness temperature thresholding of severe versus nonsevere storms during 1979, 1980 and the two years combined, respectively. The preliminary indication is that a future geostationary passive microwave imaging capability at 37 GHz (or possibly higher frequencies), with sufficient spatial and temporal resolution, would facilitate the detection and monitoring of severe convective storms. This capability would provide a useful complement to radar, especially over most of the globe which is not covered by radar.
Abstract
Autocorrelation and variance statistics were calculated for seven types of wind data in the western hemispheric tropics. Most of these data came from the Global Weather Experiment (GWE) in January 1979. They were: 1) cloud motion measurements from four different sources, 2) rawinsonde wind reports, 3) synoptic land station reports, 4) marine ship reports, 5) aircraft pilot reports, 6) automatic aircraft reports for the GWE, and 7) Seasat scatterometer winds from September 1978. Winds were analyzed within a target area from 30°N to 30°S latitude and 0° to 180°W longitude.
The Seasat scatterometer data had the highest autocorrelations and lowest standard deviations over short distances (<500 km). Cloud motions and rawinsondes had lower autocorrelations than Seasat, while synoptic land stations, ship reports, and aircraft pilot reports had the poorest autocorrelations. These correlations imply that synoptic land stations, ship reports, and aircraft reports were either more sensitive to small‐scale fluctuations than other sensors, or had higher intrinsic noise levels. Structure function plots of autocovariances against separation distance between observations indicated that Seasat was most sensitive to wind field structure by having low autovariance at short distances (100 km) that also grew with distance. The other structure function plots for low‐level wind observations indicated a lack of structure sensitivity to scalar wind speeds because of very small growth rates of the autocovariances with distance. However, all observations were sensitive to structure in the wind direction patterns.
Abstract
Autocorrelation and variance statistics were calculated for seven types of wind data in the western hemispheric tropics. Most of these data came from the Global Weather Experiment (GWE) in January 1979. They were: 1) cloud motion measurements from four different sources, 2) rawinsonde wind reports, 3) synoptic land station reports, 4) marine ship reports, 5) aircraft pilot reports, 6) automatic aircraft reports for the GWE, and 7) Seasat scatterometer winds from September 1978. Winds were analyzed within a target area from 30°N to 30°S latitude and 0° to 180°W longitude.
The Seasat scatterometer data had the highest autocorrelations and lowest standard deviations over short distances (<500 km). Cloud motions and rawinsondes had lower autocorrelations than Seasat, while synoptic land stations, ship reports, and aircraft pilot reports had the poorest autocorrelations. These correlations imply that synoptic land stations, ship reports, and aircraft reports were either more sensitive to small‐scale fluctuations than other sensors, or had higher intrinsic noise levels. Structure function plots of autocovariances against separation distance between observations indicated that Seasat was most sensitive to wind field structure by having low autovariance at short distances (100 km) that also grew with distance. The other structure function plots for low‐level wind observations indicated a lack of structure sensitivity to scalar wind speeds because of very small growth rates of the autocovariances with distance. However, all observations were sensitive to structure in the wind direction patterns.
