Search Results
You are looking at 1 - 8 of 8 items for
- Author or Editor: Alan McNab x
- Refine by Access: All Content x
Abstract
Water and energy budgets are presented for four broad classes of summertime cumulus convection. The budgets are calculated from National Hail Research Experiment rawinsonde data. The convection classifications are based on radar and precipitation data. A derivation of the budget equation is presented in order to point out two terms that are related to the rapid time changes and large horizontal gradients that sometimes occur over the budget area.
Weak, moderate and precipitating classes of convection produced apparent sources of water and energy that are generally similar to the results of other budget studies based on oceanic and/or tropical data. The results for the developing class of convection demonstrate that a cloud storage term must be included when studying convection that is rapidly changing from weak to moderate or precipitating.
Abstract
Water and energy budgets are presented for four broad classes of summertime cumulus convection. The budgets are calculated from National Hail Research Experiment rawinsonde data. The convection classifications are based on radar and precipitation data. A derivation of the budget equation is presented in order to point out two terms that are related to the rapid time changes and large horizontal gradients that sometimes occur over the budget area.
Weak, moderate and precipitating classes of convection produced apparent sources of water and energy that are generally similar to the results of other budget studies based on oceanic and/or tropical data. The results for the developing class of convection demonstrate that a cloud storage term must be included when studying convection that is rapidly changing from weak to moderate or precipitating.
Abstract
Four numerical experiments are conducted using a mesoscale primitive equation model. The experiments illustrate the broad spectrum of applications made possible by the model's flexibility in treating subgrid-scale parameterizations, lateral boundary conditions and physical processes appropriate to the scale of each simulation. One experiment uses real synoptic-scale data to produce a 12 h forecast that is compared to the observed circulation and precipitation patterns. The other experiments are initialized with idealized flows over areas ranging in size from regional to small mesoscale. The idealized flow simulations produce qualitatively realistic features such as Ice side troughs and sea, lake and mountain-valley breezes resulting from differential thermal forcing.
Abstract
Four numerical experiments are conducted using a mesoscale primitive equation model. The experiments illustrate the broad spectrum of applications made possible by the model's flexibility in treating subgrid-scale parameterizations, lateral boundary conditions and physical processes appropriate to the scale of each simulation. One experiment uses real synoptic-scale data to produce a 12 h forecast that is compared to the observed circulation and precipitation patterns. The other experiments are initialized with idealized flows over areas ranging in size from regional to small mesoscale. The idealized flow simulations produce qualitatively realistic features such as Ice side troughs and sea, lake and mountain-valley breezes resulting from differential thermal forcing.
Abstract
We compare the Completeness and quality of one year's daily temperature and precipitation observations from the National Climatic Data Center (NCDC) database with those received by the Illinois State Water Survey's (ISWS) Climate Assistance Service (CLASS). The NCDC data are keyed by hand from the E-15 forms received from the National Weather Service (NWS) Cooperative Observes, and are quality controlled. The CLASS data are transmitted by NWS Cooperative Observers via touch-tone phone to a Water Survey computer. These data are quality controlled only to the extent that each value is verbally repeated to the transmitting observer by voice synthesizer, and the ISWS compute checks to determine that the temperatures are consistent, i.e., maximum ≥ minimum and so on.
The NCDC database was more complete than that of CLASS. This situation was most obvious for precipitation entries where CLASS observers typically made no precipitation entry (rather than transmitting a zero) to save transmission time. National Climatic Data Center, on the other hand, automatically enters zeros when precipitation was unreported but was unlikely to have occurred. The NCDC database which is rigidly quality controlled was also more accurate (only 0.4% errors) that class of CLASS (3.6% errors).
It is possible that if telephone transmission became the official mechanism for data entry, missing data would be much reduced; and if each entry value had to be repeated by the observer, the accuracy of such transmissions would also improve.
Abstract
We compare the Completeness and quality of one year's daily temperature and precipitation observations from the National Climatic Data Center (NCDC) database with those received by the Illinois State Water Survey's (ISWS) Climate Assistance Service (CLASS). The NCDC data are keyed by hand from the E-15 forms received from the National Weather Service (NWS) Cooperative Observes, and are quality controlled. The CLASS data are transmitted by NWS Cooperative Observers via touch-tone phone to a Water Survey computer. These data are quality controlled only to the extent that each value is verbally repeated to the transmitting observer by voice synthesizer, and the ISWS compute checks to determine that the temperatures are consistent, i.e., maximum ≥ minimum and so on.
The NCDC database was more complete than that of CLASS. This situation was most obvious for precipitation entries where CLASS observers typically made no precipitation entry (rather than transmitting a zero) to save transmission time. National Climatic Data Center, on the other hand, automatically enters zeros when precipitation was unreported but was unlikely to have occurred. The NCDC database which is rigidly quality controlled was also more accurate (only 0.4% errors) that class of CLASS (3.6% errors).
It is possible that if telephone transmission became the official mechanism for data entry, missing data would be much reduced; and if each entry value had to be repeated by the observer, the accuracy of such transmissions would also improve.
Abstract
Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Initial correlation analyses revealed that data from 700 mb were sufficient to represent the upper-air or background climate. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%–95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. The R 2's for the GHCN models ranged between 0,86 and 0.95, whereas the R 2's for the COOP models ranged between 0.64 and 0.92. In addition, root-mean-square errors (rmse's) were over 3°C for GHCN models and over 2°C for COOP models for winter months, and near 2°C for GHCN models and near 1.5°C for COOP models for summer months. The results of this study—a large amount of explained variance and a relatively small rmse—indicate the usefulness of these models for predicting surface temperatures. Urban landscape data are incorporated into these models in Part II of this study to estimate the urban bias of surface ternperatures.
Abstract
Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Initial correlation analyses revealed that data from 700 mb were sufficient to represent the upper-air or background climate. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%–95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. The R 2's for the GHCN models ranged between 0,86 and 0.95, whereas the R 2's for the COOP models ranged between 0.64 and 0.92. In addition, root-mean-square errors (rmse's) were over 3°C for GHCN models and over 2°C for COOP models for winter months, and near 2°C for GHCN models and near 1.5°C for COOP models for summer months. The results of this study—a large amount of explained variance and a relatively small rmse—indicate the usefulness of these models for predicting surface temperatures. Urban landscape data are incorporated into these models in Part II of this study to estimate the urban bias of surface ternperatures.
Abstract
Atmospheric sounding products from NOAA's polar-orbiting satellites were used to derive and test predictive equations of rural shelter-level maximum and minimum temperatures. Sounding data from both winter and summer months were combined with surface data from over 5300 cooperative weather stations in the continental United States to develop multiple linear regression equations. Separate equations were developed for both maximum and minimum temperature, using the three types of sounding retrievals (clear, partly cloudy, and cloudy). Clear retrieval models outperformed others, and maximum temperatures were more accurately predicted than minimums. Average standard deviations of observed rural shelter temperatures within sounding search areas were of similar magnitude to root-mean-square errors from satellite estimates for most clear and partly cloudy cases, but were significantly less for cloudy retrieval cases. Model validation for surrogate polar and tropical climatic regions showed success in application of the four clear retrieval models (maximum and minimum temperature, for both winter and summer). This indicates the potential adaptability of these models to estimates of rural shelter temperature in areas outside of the United States.
Abstract
Atmospheric sounding products from NOAA's polar-orbiting satellites were used to derive and test predictive equations of rural shelter-level maximum and minimum temperatures. Sounding data from both winter and summer months were combined with surface data from over 5300 cooperative weather stations in the continental United States to develop multiple linear regression equations. Separate equations were developed for both maximum and minimum temperature, using the three types of sounding retrievals (clear, partly cloudy, and cloudy). Clear retrieval models outperformed others, and maximum temperatures were more accurately predicted than minimums. Average standard deviations of observed rural shelter temperatures within sounding search areas were of similar magnitude to root-mean-square errors from satellite estimates for most clear and partly cloudy cases, but were significantly less for cloudy retrieval cases. Model validation for surrogate polar and tropical climatic regions showed success in application of the four clear retrieval models (maximum and minimum temperature, for both winter and summer). This indicates the potential adaptability of these models to estimates of rural shelter temperature in areas outside of the United States.
Abstract
A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.
Abstract
A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.
The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5° × 2.5° latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.
The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5° × 2.5° latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.
Abstract
Urban temperature bias, defined to be the difference between a shelter temperature reading of unknown but suspected urban influence and some appropriate rural reference temperature, is estimated through the use of polar-orbiting satellite data. Predicted rural temperatures, based on a method developed using sounding data, are shown to be of reasonable accuracy in many cases for urban bias assessments using minimum temperature data from selected urban regions in the United States in July 1989. Assessments of predicted urban bias were based on comparisons with observed bias, as well as independent measures of urban heat island influence, such as population statistics and urban-rural differences in a vegetation index. This technique provides a means of determining urban bias in regions where few if any rural reference stations are available, or where inhomogeneities exist in land surface characteristics or rural station locations.
Abstract
Urban temperature bias, defined to be the difference between a shelter temperature reading of unknown but suspected urban influence and some appropriate rural reference temperature, is estimated through the use of polar-orbiting satellite data. Predicted rural temperatures, based on a method developed using sounding data, are shown to be of reasonable accuracy in many cases for urban bias assessments using minimum temperature data from selected urban regions in the United States in July 1989. Assessments of predicted urban bias were based on comparisons with observed bias, as well as independent measures of urban heat island influence, such as population statistics and urban-rural differences in a vegetation index. This technique provides a means of determining urban bias in regions where few if any rural reference stations are available, or where inhomogeneities exist in land surface characteristics or rural station locations.
