Engineering topics

Bloomfield, Peter

Publications and source records attributed to Bloomfield, Peter.

6 records

NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

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 (ECMWF) 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. 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. 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.

Epperson, David L.↗

NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 2: Estimation of the urban bias

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 (ECMWF) 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 x 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 deg x 1 deg 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 (COOP) Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network (GHCN). 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. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available.

Epperson, David L.↗

NASA NTRS1994

Estimating urban temperature bias using polar-orbiting satellite data

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.

Johnson, Gregory L.↗

NASA NTRS1993

The use of polar-orbiting satellite sounding data to estimate rural maximum and minimum temperatures

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.

Johnson, Gregory L.↗

NASA NTRS1991

Total ozone trends deduced from Nimbus 7 TOMS data

The Total Ozone Mapping Spectrometer (TOMS) on the Nimbus 7 satellite has been measuring the total column amount of ozone over the globe for more than 11 years. Recent improvements in the data analysis have led to a technique for determining and removing drift in the calibration such that the data at the end of the record are precise to + or - 1.3 percent (2-sigma) relative to the data at the beginning of the record. A statistical model, including terms for seasonal variation, linear trend, quasi-biennial oscillation, solar cycle and second-order autoregressive noise has been fit to the TOMS time series of total ozone data. The linear trend obtained when this statistical model is fit to the TOMS data averaged between 65 N and 65 S latitudes is -0.26 + or - 0.14 percent/year or -3 percent over the 11.6 year time period from November 1978 to May 1990. The trend is near zero (0.0002 + or - 0.2 percent/year) at the equator and increases toward both poles.

Stolarski, Richard S.↗

NASA NTRS1988

Wintertime losses of ozone in high northern latitudes

Total column ozone data over the past 22 to 30 years from ground-based Dobson and filter ozonometer stations between 30 N and 80 N are analyzed for residual trends remaining after allowance for the known geophysical variations corresponding to: (1) the change of seasons; (2) the quasi-biennial oscillation; and (3) the 11-year solar sunspot cycle. Examination of the data from several ground stations between 45 and 55 N indicated a seasonal difference in the long-term ozone series, with statistically significant losses in several winter months. Accordingly, the data from individual stations were analyzed with multiple regression analysis, seeking trends on a monthly basis after allowance for the known geophysical cycles. Previous statistical analyses were conducted as tests of 1-D model calculations which do not show any differences with the seasons, and any trend toward change in ozone concentrations is expressed in a yearly trend without seasonal variation. Such a model is inappropriate for calculations with a data set which exhibits a pronounced tendency toward seasonal differences in the trends. Comparisons with model calculations then require 2-D models into which seasonal and latitudinal differences can easily be programmed.

Harris, Neil↗