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Jerry M. Davis
and
Paul N. Rappoport

Abstract

Using an exponential smoothing procedure and an autoregressive-moving average process; forecasts for the monthly Palmer Drought Severity Index were calculated. The autocorrelation and partial autocorrelation functions of severity index values were used as a starting point for the autoregressive-moving average model selection process. Of the many possible autoregressive-moving average models, the one that was selected provided the best forecasts based on the mean square error. Monthly data for the period 1929–1969 were utilized in a nonlinear least-squares computer routine to arrive at estimated parameter values for the autoregressive-moving average model. Monthly forecasts with a lead time of one month were generated using the exponential smoothing and autoregressive-moving average procedures for the period 1970–1972. These forecasts were compared with the myopic (persistence) forecasts, X t+1=X t . The mean square errors of the forecasts were 0.63 for the autoregressive-moving average model, 0.65 for the myopic model, and 0.79 for the exponential smoothing model. From the mean-square-error calculations, it appears that there is no statistically significant difference between the forecasts given by the Box-Jenkins and myopic models; however, the 95% confidence intervals for these two models overlap only slightly during the first part of the forecast period indicating that there may be some advantage to using the Box-Jenkins model instead of the myopic model. Both of these models are superior to the exponential smoothing model. These results demonstrate the usefulness of the relatively new autoregressive-moving average time series analysis procedures.

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Mark DeMaria
,
Jerry M. Davis
, and
Donna M. Wojtak

Abstract

The Portable Automated Mesonet (PAM) data obtained during the Genesis of Atlantic Lows Fxperiment (GALE) are used to document mesoscale wave activity during the 3-day period from 4 to 6 February 1986. From the surface pressure analyses, four cases of wave activity are identified with wavelengths of 200–400 km, phase speeds of 20–40 m s−1 and trough-to-crest pressure amplitudes of 0.5–3.5 mb. Precipitation was associated with the waves in two of the four cases. Detailed analyses of the horizontal structure show that the waves do not have the pressure-wind relationship expected from linear gravity wave theory. The wind vectors are oriented from high to low pressure with a maximum amplitude between the high and low pressure areas.

Low-level inversions were present in three of the four cases. In the raw without a low-level inversion, the amplitude rapidly decreased as the wave moved towards the east. In the case which lasted for the longest time period (at least 8 h). and had the largest pressure amplitude, the sounding bad a critical level (where the wind speed equated the wave speed) and a level where the Richardson number was less than 0.25. Vertical velocities as large as 30 cm s−1 were observed and them was some evidence that the wave was vertically tiled towards its direction of motion.

Complex principal component analysis (CPCA) is applied to the surface pressure data to determine the applicability of this technique to the study of mesoscale waves. It is shown that CPCA could be used to generalize the results of this study to the entire 60-day period of GALE.

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Allen J. Riordan
,
Jerry M. Davis
, and
Raymond B. Kiess

Abstract

Six years of tower data from two dissimilar sites in the eastern piedmont of the Carolinas are analyzed to yield a selective climatology of the lower portion of the morning inversion. Its transition to daytime conditions is then described and statistically modeled.

Both sites are in clearings surrounded by forest, but one site is in a valley by a lake, while the other, 175 km to the north, is on a low hilltop. Measurements of wind speed and direction, the standard deviation of wind direction, dew point, and temperature at 11 m, temperature difference (ΔT) between 11 and 60 m, plus solar radiation, were analyzed for an 8-h period starting from three hours before local sunrise each day for both locations.

Results show that predawn inversions characterize over 70% of the data and strong inversions of over 5°C per 100 m in the tower layer characterize 30% of the mornings at the hilltop site. At the valley site, strong inversions are less common, probably because of the proximity of the lake. There is a correlation of 0.71 in daily site-to-site ΔT at dawn. This suggests strong overall synoptic control of the local inversion frequency.

The transition to well-mixed conditions after sunrise depends chiefly on ΔT prior to sunrise. Analysis of mean trends in variables during the transition shows it is a remarkably well-ordered process. The time from sunrise to a mean isothermal state (between 11 and 60 m only) takes about 1 to 2 h.

Daily transition is predicted by a linear regression scheme based on predawn conditions and developed and tested separately at each site. Chief predictors are inversion intensity, dew point and 60 m wind speed. For cloudy mornings the rms error for the prediction time from sunrise to mean isothermal conditions is 0.3 h. For days with variable cloudiness, a rather unspectacular R 2 value of 0.3 to 0.4 is, nevertheless, statistically significant. A similarity in models at both sites is noted. In cloudless conditions the models are, in fact, nearly interchangeable.

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Brian K. Eder
,
Jerry M. Davis
, and
Peter Bloomfield

Abstract

This paper utilizes a two-stage (average linkage then convergent k means) clustering approach as part of an automated meteorological classification scheme designed to better elucidate the dependence of ozone on meteorology. When applied to 10 years (1981–90) of meteorological data for Birmingham, Alabama, the classification scheme identified seven statistically distinct meteorological regimes, the majority of which exhibited significantly different daily 1-h maximum ozone concentration distributions. Results from this two-stage clustering approach were then used to develop seven “refined” stepwise regression models designed to 1) identify the optimum set of independent meteorological parameters influencing the O3 concentrations within each meteorological cluster, and 2) weigh each independent parameter according to its unique influence within that cluster. Large differences were noted in the number, order, and selection of independent variables found to significantly contribute (α = 0.10) to the variability of O3. When this unique dependence was taken into consideration through the development and subsequent amalgamation of the seven individual regression models, a better parameterization of O3's dependence on meteorology was achieved. This “composite” model exhibited a significantly larger R 2 (0.59) and a smaller rmse (12.80 ppb) when compared to results achieved from an “overall” model (R 2 = 0.53, rmse = 13.85) in which the meteorological data were not clustered.

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David A. Barber
,
Jerry M. Davis
, and
Allen J. Riordan

Abstract

A substantial decline in North American cyclone and anticyclone activity has been documented by several recent studies based on counts of disturbance tracks. An independent method of assessing long-term trends in synoptic-scale activity based on sequential spectral analysis of station pressure is suggested. The efficacy of this approach is supported by previous studies relating the spatial distribution of variance of band-pass filtered pressures to preferred cyclone tracks. However, examples of a preliminary application of the spectral method to three widely separated stations using approximately 30 years of winter data fail to reveal any significant long-term trends in the variance of pressure for synoptic-scale time periods.

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Gregory L. Johnson
,
Jerry M. Davis
,
Thomas R. Karl
,
Alan L. McNab
,
Kevin P. Gallo
,
J. Dan Tarpley
, and
Peter R. Bloomfield

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.

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Gregory L. Johnson
,
Jerry M. Davis
,
Thomas R. Karl
,
Alan L. McNab
,
J. Dan Tarpley
, and
Peter Bloomfield

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.

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David L. Epperson
,
Jerry M. Davis
,
Peter Bloomfield
,
Thomas R. Karl
,
Alan L. McNab
, and
Kevin P. Gallo

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.

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David L. Epperson
,
Jerry M. Davis
,
Peter Bloomfield
,
Thomas R. Karl
,
Alan L. McNab
, and
Kevin P. Gallo

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.

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M. Ades
,
R. Adler
,
Rob Allan
,
R. P. Allan
,
J. Anderson
,
Anthony Argüez
,
C. Arosio
,
J. A. Augustine
,
C. Azorin-Molina
,
J. Barichivich
,
J. Barnes
,
H. E. Beck
,
Andreas Becker
,
Nicolas Bellouin
,
Angela Benedetti
,
David I. Berry
,
Stephen Blenkinsop
,
Olivier. Bock
,
Michael G. Bosilovich
,
Olivier. Boucher
,
S. A. Buehler
,
Laura. Carrea
,
Hanne H. Christiansen
,
F. Chouza
,
John R. Christy
,
E.-S. Chung
,
Melanie Coldewey-Egbers
,
Gil P. Compo
,
Owen R. Cooper
,
Curt Covey
,
A. Crotwell
,
Sean M. Davis
,
Elvira de Eyto
,
Richard A. M de Jeu
,
B.V. VanderSat
,
Curtis L. DeGasperi
,
Doug Degenstein
,
Larry Di Girolamo
,
Martin T. Dokulil
,
Markus G. Donat
,
Wouter A. Dorigo
,
Imke Durre
,
Geoff S. Dutton
,
G. Duveiller
,
James W. Elkins
,
Vitali E. Fioletov
,
Johannes Flemming
,
Michael J. Foster
,
Richard A. Frey
,
Stacey M. Frith
,
Lucien Froidevaux
,
J. Garforth
,
S. K. Gupta
,
Leopold Haimberger
,
Brad D. Hall
,
Ian Harris
,
Andrew K Heidinger
,
D. L. Hemming
,
Shu-peng (Ben) Ho
,
Daan Hubert
,
Dale F. Hurst
,
I. Hüser
,
Antje Inness
,
K. Isaksen
,
Viju John
,
Philip D. Jones
,
J. W. Kaiser
,
S. Kelly
,
S. Khaykin
,
R. Kidd
,
Hyungiun Kim
,
Z. Kipling
,
B. M. Kraemer
,
D. P. Kratz
,
R. S. La Fuente
,
Xin Lan
,
Kathleen O. Lantz
,
T. Leblanc
,
Bailing Li
,
Norman G Loeb
,
Craig S. Long
,
Diego Loyola
,
Wlodzimierz Marszelewski
,
B. Martens
,
Linda May
,
Michael Mayer
,
M. F. McCabe
,
Tim R. McVicar
,
Carl A. Mears
,
W. Paul Menzel
,
Christopher J. Merchant
,
Ben R. Miller
,
Diego G. Miralles
,
Stephen A. Montzka
,
Colin Morice
,
Jens Mühle
,
R. Myneni
,
Julien P. Nicolas
,
Jeannette Noetzli
,
Tim J. Osborn
,
T. Park
,
A. Pasik
,
Andrew M. Paterson
,
Mauri S. Pelto
,
S. Perkins-Kirkpatrick
,
G. Pétron
,
C. Phillips
,
Bernard Pinty
,
S. Po-Chedley
,
L. Polvani
,
W. Preimesberger
,
M. Pulkkanen
,
W. J. Randel
,
Samuel Rémy
,
L. Ricciardulli
,
A. D. Richardson
,
L. Rieger
,
David A. Robinson
,
Matthew Rodell
,
Karen H. Rosenlof
,
Chris Roth
,
A. Rozanov
,
James A. Rusak
,
O. Rusanovskaya
,
T. Rutishäuser
,
Ahira Sánchez-Lugo
,
P. Sawaengphokhai
,
T. Scanlon
,
Verena Schenzinger
,
S. Geoffey Schladow
,
R. W Schlegel
,
Eawag Schmid, Martin
,
H. B. Selkirk
,
S. Sharma
,
Lei Shi
,
S. V. Shimaraeva
,
E. A. Silow
,
Adrian J. Simmons
,
C. A. Smith
,
Sharon L Smith
,
B. J. Soden
,
Viktoria Sofieva
,
T. H. Sparks
,
Paul W. Stackhouse Jr.
,
Wolfgang Steinbrecht
,
Dimitri A. Streletskiy
,
G. Taha
,
Hagen Telg
,
S. J. Thackeray
,
M. A. Timofeyev
,
Kleareti Tourpali
,
Mari R. Tye
,
Ronald J. van der A
,
Robin, VanderSat B.V. van der Schalie
,
Gerard van der SchrierW. Paul
,
Guido R. van der Werf
,
Piet Verburg
,
Jean-Paul Vernier
,
Holger Vömel
,
Russell S. Vose
,
Ray Wang
,
Shohei G. Watanabe
,
Mark Weber
,
Gesa A. Weyhenmeyer
,
David Wiese
,
Anne C. Wilber
,
Jeanette D. Wild
,
Takmeng Wong
,
R. Iestyn Woolway
,
Xungang Yin
,
Lin Zhao
,
Guanguo Zhao
,
Xinjia Zhou
,
Jerry R. Ziemke
, and
Markus Ziese
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