Solar climates of the conterminous united states: A preliminary investigation

Solar Eneroy Vol. 24. pp. 295-303 ~) Pergamon Press Ltd.. 1980. Printed in Great Britain

0038-092X/80~301-0295502.00.~

SOLAR CLIMATES OF THE CONTERMINOUS UNITED STATES: A PRELIMINARY INVESTIGATION CORT J. WILLMOTr and MARK T. VERNON Center for Climatic Research, Department of Geography, University of Delaware, Newark, DE 19711, U.S.A.

(Received 31 August 1979; accepted 18 October 1979) Abstract--A classification of solar climates in the conterminous United States is developed using a combination of analyses which include: P-mode principal components analysis of a covariancc matrix and an optimized Ward's grouping of six non-trivial dimensions of solar climate. While traditional classifications are based upon summary statistics (e.g. means) and often neglect many elements of temporal and spatial variability, the taxonomy reported on her~ incorporates the average magnitudes of global radiation as well as the stations' day-to-day perturbations. Ten solar climates are identified, described and presented in map form. Data for the study were 5 yr of daily total global radiation observed at 60 stations from 1970 to 1974.

I. INTRODUCTION Characterizing climate by classifying one or more climatological parameters or characteristics has been a concern of climatologists for at least a century 1'1]. Although the previous literature is extensive, much of the work is now inadequate since "most schemes are based upon theoretically weak and/or arbitrary decisions about (1) the number of regimes that exist, (2) what constitutes a boundary and/or where should it be drawn, (3) which parameters of climate should he used to characterize climate and (4) how those parameters should be summarized prior to the classifieation process in order that bias will he minimized" 1'2]. Until very recently most of these problems could not be adequately solved because computational means and taxometric theory were not sufficiently developed. Now, however, advances in numerical classification coupled with increased data availability, e.g. global radiation, allow for the "objective" classification of climate. A m e extensive discussion of the practical importance, history and methods of modern climatic classification is contained in Willmott [:2] and, therefore, will not be considered in great detail here. In response to the many considerations mentioned above, and point (3) in particular as solar radiation is probably the most important climatic variable, we present the procedures and results of what we believe is the first objective and comprehensive classification of solar climates in the United States. The important, but more subjective, contribution of Terjung 1'3] to world solar climatology should be noted, however. Since the classification is based upon a particular solar radiation data set as well as the algorithms employed, the accuracy and nature of these elements will be briefly examined followed by an in depth interpretation of the results. It is hoped that both the 295

methods and final classification will be of value to all those interested in the spatial and temporal vicissitudes of solar radiation.

2. DATA Data for the classification are contained in the National Climatic Center's Card Deck 480 I4]. Despite Card Deck 480's long documented calibration and other errors, at the time it was selected for analysis it provided perhaps the most extensive spatialtemporal coverage available. Moreover, because the analyses reported on here are trend seeking in nature, based upon an extensive data set and presented at a gross (national) scale, most of the errors are believed to have an insignificant adverse impact on the classification. Even the most severe of the documented longterm measurement errors, for instance, would be confined to a particular instrument and/or station. Such a record, therefore, could only exert an adverse influence on the classification in a magnitude proportional to that station's error mean and variance. Since the input data set, which is analyzed as a whole, is comprised of 60 stations and 5 yr of record at each station, i t is highly improbable that any such error could contribute even 1 per cent to the formation of the solar climatic regimes and regions discussed here. This should become more apparent in subsequent text. Daily totals of global radiation for 60 stations from 1970 to 1974 were selected from Card Deck 480 based upon the assumption that fewer than 5 yr of record were insufficient to adequately characterize year-toyear solar variability. More than a 5-yr criterion would have sharply reduced the number of eligible stations, Seven to 15 yr of record is perhaps closer to

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the optimal number[5] although 5yr proved to produce a reasonably stable taxonomy during preliminary testing. There is an ongoing debate among climatologists about what length of record best characterizes the present climate although most would argue that it is place, time and variable specific. As a result, the choice of a 5-yr temporal dimension may be considered somewhat meager but it was necessary to assure that records overlapped at a sufficient number of stations to give regional definition. Nonetheless, better classifications of solar radiation cannot be produced until reliable solar data become available for longer periods of time at all stations in a much more dense spatial network. Once the 60 x 1826 solar data matrix was formulated, missing observations had to be estimated before the computational analyses could be performed. Missing observations numbered around 200 at five stations and significantly less than that at all other stations. Although a number of interpolation methods could be employed, these missing values were estimated by the mean of all "good" observations for that same day at that same station over the period of record. Adjacent days were not used as there were a large number of successive missing days and we wanted to be consistent in our interpolation computations. With these missing values estimated, the data set was ready for analysis. 3. SIMILARITY BETWEEN STATION R E C O R D S

As the 60 x 1826 data matrix was to be evaluated by a P-mode principal components analysis (discussed in the next section), a prerequisite step was to select the appropriate measure of similarity between station records. Consistent with Willmott l"6], the covariance coefficient (c) was chosen as all variables (station records) had the same units (M J/m2). Most often, Pearson's Product Moment correlation coefficient (r) is used as the input to a components analysis although the standardization process serves only to remove pertinent information when the analysis is P-mode[6]. Neither coefficient, unfortunately, uniquely describes similarities between time-series although again, c contains more information than r. In other words, dissimilar pairs of solar time-series may produce the same value of c or r thereby making interpretation a bit more complex. Further research needs to be directed toward the development of better similarity coefficients. A symmetric (60 × 60) similarity matrix of covariance coefficients (C), which contained all combinations of covariations between the 60 station records, was then formulated and it served as input to the components analysis.

the significant and independent (orthogonal) dimensions or components of solar variability in the conterminous United States. This was accomplished through a P-mode principal components analysis of C - - a brief verbal description of which ensues. A more complete description of P-mode principal components analysis is contained in Willmott [6]. Principal components analysis is a set of linear operations performed on a symmetric similarity matrix such as that described above [7, 8]. The m original variables or dimensions (60 in this study) are translated into p "significant" dimensions (components) and m-p "insignificant" dimensions where p is usually considerably less than m. These new variables or components are orthogonal to each other, and each succeeding component explains as much of the total remaining variance as possible. Components analysis results in a consolidation of the total amount of information in the data set because most of the variance contained in the m original variables is now explained by p components. Although there are as many components as there are original variables, the significance of each succeeding component decreases (i.e. each component explains less variance than the previous one), so that the latter m--p components may be neglected. A five-component solution was chosen as optimal, according to Catteli's [9] scree test. This test is based on an evaluation of the rate of decline of the explained variance with each succeeding component. Beyond a point where the slope of the explained variance curve levels off, components may be ignored. Of the total variance contained in the original data, 74 per cent is explained by the first five components. It is thought that the many errors in Card Deck 480 contributed to this relatively large unexplained variance, i.e. 26 per cent. The spatial distribution of variance explained at each station is seen in the contour map, Fig. 1. The importance of the five components is that each describes a pattern of variability caused by mechanisms that affect radiation receipt. A few of the more important mechanisms are discussed later in the paper. EXPLAINED

VARIANCE

4. D E T E R M I N I N G T H E D I M E N S I O N S O F SOLAR CLIMATE

Following the computation of C, the next step in the classification process was to objectively describe

Fig. 1. Spatial distribution of the variance contained in each 5-yr solar record explained by the five-component solution.

Solar climates of the conter~neas United States One problem which can adversely influence a components analysis is that of an ill-conditioned covariance matrix. An ill-conditioned matrix can occur when two or more stations in the matrix are highly covaried and it can make a component more a function of the computing algorithm than the significant patterns of variation within the data. With this in mind, the data set was analyzed without the Seattle, Washington, station because of its high degree of colinearity with the Seattle-Tacoma Airport station. The final regionalization remained stable under these conditions and it was felt that this result confirmed an adequate conditioning of the 60-station network. In addition to the P-mode components evaluation of the spatial-temporal variability in solar radiation, an squally important climate-determining characteristic of solar radiation is its mean. In response to this fact, the 5-yr mean values of global radiation for each station are included in the taxonomy as a sixth dimension. It should be noted that the component loadings, which are used to characterize the components or the patterns of solar variability, have the same units as the original variables (MJ/m 2) and, therefore, are directly comparable to the station means. The input to the final stage of the classification is, then, a (6 x 60) matrix comprised of five significant components and a dimension of the 5-yr station means. 5.

WARD'S GROUPING (OPTIMIZED) OF THE SIX DIMENSIONS,OF SOLAR CLIMATE

According to Wishart [10], Ward's ['11~] Algorithm is among the best hierarchicai-agglomerative group-

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ing schemes available. Previous use and evaluation of the procedure by Willmott [6] and Vernon [12] concurred with Wishart and Ward's Algorithm was subsequently selected as the procedure used to group the six dimensions of solar climate. The case-wise clustering procedure, at each stage in the grouping process, fuses those groups and/or stations that minimize the error sum of squares (ESS). ESS is defined as

ESS = X i=1 j = l

su

where n is the number of observations (points in sixspace) in group i, k is the number of groups and S U is an Euclidean distance from a data point in six-space to its group centroid. Six-space refers to the cartesian space defined by the five components and the station means where each station (case) represents a single data point having the coordinates of its five component ioadings and mean. The grouping then, in essence, fu~es those two clusters (a cluster may consist of one or more data points) in six-space, at each step in the agglomerative process, that minimize the total sum of the squared distances from each data point to its group centroid. For the 60-station network, Ward's hierarchical solution, was determined to be 10 clusters (solar climates) based upon a subjective evaluation of the d e n d r o g r a m (Fig. 2) and a semi-log graph of the levels of fusion (twice the increase in the ESS caused by the fusion of two groups) vs the number of clusters. Most often, this involves locating one of the largest increases in ESS, or greatest losses of information,

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Fig. 2. Dandrogram showing all levels of fusion and the initial (non-optimize) 10 = regime solution. All fusions were based upon Ward's [11] criterion. Stations to which the station numbers correspond can he found in Vernon [12]. The level of fusion has the units 2(cal/cm')2.

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fORT J. WILLMOTT and MARK T. VERNON

SOLAR CLIMATES OF THE CONTIGUOUS UNITED S TA TES

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Fig. 3. Proximal map of the t0 solar climates in the conterminous United States based upon an optimized Ward's [10] grouping of five principal components and the station means.

which occurred during a single step in the fusion process. The previous solution (i.e. k - 1 groups) is then regarded as best. Other factors, however, such as the previous climatic literature and the fact that information contained in the groupings made at earlier stages in the process is lost, should and did influence the selection of a 10-regime solution. Indeed the 10-regime pattern was chosen over the graphically more plausible 6-regime solution on the basis that 10 regimes are more consistent with the previous literature 1-13-1 and the information contained in 6 regimes is also encapsulated in 10 regimes (Fig. 2). More objective ways of selecting the "correct" number of groups are being explored in the computational sciences and statistics, although no theory which would be appropriate here has yet emerged--to the authors' knowledge. After 10 clusters had been established as the correct number, the station membership in each group was iteratively optimized--again using Ward's criterion as the constraint• This involved, within the 10-group framework, reassigning stations to different groups until the associated ESS was minimized [10]. Only a very few stations changed their group affiliation as a result of optimization although the reader should note that the dendrogram (Fig. 2) describes Ward's hierarchical solution while the final classification is based upon the optimized calculations. Solar climates which emerged from these computations were proximally mapped using SYMAP (Fig. 3).

6• INTERPRETATION OF THE SIX DIMENSIONS OF SOLAR CLIMATE

An analysis of the radiation characteristics of each of the 10 solar climates rests, in part, on an examination of the five principal components as well as the station means• Each component represents a distinct pattern of variation in the radiation time-series and the loadings describe the covariations between each

station and each component. Components may be represented spatially by interpolating between the point values of the loadings at each station and temporally by plotting the component scores. The Ioadings were subsequently mapped and the scores plotted. With the exception of Component 1, the time-series plots of the component scores revealed too little information to be useful in a physical interpretation of the components. They (the scores) are extremely complex with innumerable perturbations while their variances are quite small. Smoothing by running-means did not help as it became difficult to ascertain whether patterns observed in a smoothed record were "real" or merely artifacts of the degree of smoothing selected. As a result, it was decided that our interpretations of the components would be based on an examination of the spatial associations of the loadings with known regional climatic influences. Five isoline maps of the component loadings were subsequently drawn using SYMAP's contouring procedure and these show the geographical distribution of the influence of each source of variation on the 60 stations (Fig. 4). Component 1 is the only all-positive component and the highest Ioadings occur in Oregon and Idaho while the lowest occur in Florida (Fig. 4). This component describes most of the United State's global radiation regime since it explains about 62 per cent of the variance. It indicates that the greatest single factor influencing radiation receipt is seasonality--the change in the angle of incidence of the direct solar beam and the concurrent changes in daylength through the course of a year. Component 1, then, is an average seasonality component. Scasonality is exhibited mostly in the amplitude of the solar curves for the 5-yr period. The large difference in amplitude between a high loading station such as Medford, Oregon, and a low loading station such as Apalachicola, Florida, is evident when viewing these two stations' records. The annual variation in solar receipt, expressed in Component 1, is influenced on a gross scale by two factors--the latitude of a station and its annual cloud regime. Seasonality is enhanced in the Northwest, particularly east of the coast ranges, for example, because of high latitude and clear summer skies while low amplitude curves, expected in the Southeast because of the low latitudes, are further decreased by summer clouds. Only explaining approx. 4 per cent of the total variation, Component 2 is a bipolar component, i.e. the variation explained by this component describes two contrasting trends and/or regimes. It loads highest in the Northeast and grades to negative values over Kansas and Nebraska and neutral values over most of the west (Fig. 4). Although somewhat difficult to interpret, the 5-yr curves for the high (positive) loading and low (negative) loading stations appear to reflect patterns in the range and regularity of summer fluctuations of radiation. In the positively loading Northeast, a wide range of random day-to-day pertur-

Solar climates of the contermi,'mag Dnited States bations occur during the summer. Negatively loading areas such as Dodge City, Kansas, have the same range of fluctuations (the difference between receipt on a cloudy day in summer and a clear day in summer are similar) as the positively loading locations, but the negative areas exhibit a more cyclic pattern of cloudless and cloudy periods. The negatively loading regime experiences periodic summer droughts of 2-3 weeks. As one proceeds eastward from the central plains towards neutral loadings, summer cloudless periods become shorter and more irregular while a westward movement toward neutral loadings decreases the range of day-to-day, summer perturbations. In sum, positive Ioadings indicate nearly continuous, wide-range perturbations (constantly changing weather) and negative loadings describe a regular cycle of high-range perturbations and periods of lowrange perturbations. Neutral loadings indicate a more random distribution of perturbations or general lack of them. Component 3 loads highest in the central Appalachians, and its spatial distribution correlates well with the frequency of occurrence of high turbidity (Fig. 4).

COMPONENT

ONE

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This component explains between 3 and 4 per cent of the total variance and it appears to be a measure of the annual variability of turbidity where negative loading areas indicate little contrast between summer and winter turbidity. Areas near Missoula, Montana, and Idaho Falls, Idaho, have low amplitude turbidity curves and low loadings. Locations with a high annual range of turbidity are spatially coincident with the region of highest (positive) loadings on Component 3. Eastern Tennessee and the western Carolinas therefore comprise the nodal reaches of this component. Areas just to the north of the mean track of extratropical Cyclones in winter contain a node of high Ioadings on Component 4 (Fig. 4). This locale experiences many more cloudy days in winter than do stations to the west, south and east. Positive ioadings are also found in the northeast where cyclones occur with somewhat less regularity. The attenuating influence of these extratropical systems on solar radiation, particularly direct beam, diminishes as one moves to the Southeast and Southwest, and this is reflected by the negative Ioadings in these areas.

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fORT J. WILLMOTT a n d MARK T. VERNON

Component 5 describes only about 2 per cent of the total variance but its contribution to the solution is nearly as important as Components 2, 3 and 4. This evaluation was made from the results of Ward's grouping of four-, five- and six-component solutions. In particular, it was discovered that the inclusion of the sixth component, after the fifth, did not destabilize the classification. Histograms of the mean monthly number of days with 0.01 in. or more of precipitation, representing seasonal precipitation regimes across the United States, reveal a marked similarity to the spatial distribution of loadings on Component 5 (Fig. 4)[12]. High loading stations in Texas and New England, for example, receive an even distribution of days with more than 0.01 in. of precipitation throughout the year. Low loading areas in the Pacific Northwest, conversely, are seasonally variable in this precipitation parameter. Precipitation is much decreased in the summer. More moderate negative loading regions such as Wisconsin and the Carolinas show an intermediate degree of precipitation related seasonality. At least as important as the variability described in the first five principal components, are the magnitudes of solar energy contained in the 5-yr station means. When mapped (Fig. 4), the 5-yr station means exhibit a pattern familiar to all those interested in solar energy. Average daily maxima occur in the Southwest, along the Gulf coast of Texas and, to a lesser degree, in Florida. From the Southwest and South, the magnitude of insolation decreases with distance North, Northeast and Northwest. Familiar anomalies, such as (1) a southward turn of isolines along the Pacific coast--particularly along the Northwest Pacific coast, (2) increases in shortwave energy receipt in the uplands---particularly the Rockies and (3) diminished values of insolation incident on the humid east, were all present in the 5-yr means (Fig. 4). More important for this work, however, is the fact that the means significantly contributed to the identification of the solar climates during the grouping process as will be discussed in the next section. Our interpretations of the component loading maps are considered important for characterizing the solar climates but they should not be considered definitive. They are but an attempt to give the reader some physical insight by summarizing the complex set of processes and responses which caused each component, and the means, to take its particular form and magnitude, It should be remembered that the components are merely mathematical summaries of the maze of radiation exchanges and they are time and space specific. To think of them as well-defined physical phenomena is incorrect even though their characters do suggest dominant influences. Our interpretations are based upon an amalgamation of synoptic information, the 5-yr curves of global radiation, a general knowledge of the climatic characteristics of the United States and, of course, inference.

7. SOLAR CLIMATES OF THE CONTERMINOUS UNITED STATES

Ward's [11] optimized grouping of the six dimensions of solar climate is expressed in map form (Fig. 3) and it summarizes the analyses discussed previously. Since the nature of each solar climate may not be evident from the map, each of the 10 regimes will be described and interpreted, in numerical sequence, in this section of the paper. Perhaps the most outstanding radiation characteristic of Region 1 is the coincident occurrence of lowrange and low-magnitude perturbations in winter. No other solar climate has such a consistently cloudy low-sun period. Low values of radiation, normally expected in winter because of the high latitude, are further dampened by numerous winter cyclonic storms and orographic influences. On the other hand, a high degree of seasonality, because of the high latitudes, is evident and also reflected in relatively high loadings on Component 1. One problem confounding the exact definition of Region 1 is that it contains only two stations: Seattle and the Seattle-Tacoma Airport. A denser station network would probably push this region further south along the coast, however, the proximal mapping scheme is based only upon the spatial distribution of the reporting stations in order that the degree of generalization remain consistent across the map. Region 1 can be thought of as a North Pacific coastal solar climate. Region 2 is similar to Region 1, in many respects, although the magnitudes of solar radiation are not as consistently low in winter nor are there nearly as many fluctuations of radiation in summer. The annual range of perturbations is still quite large, but the number of low-energy periods is considerably fewer than in the region to the north. Seasonality is therefore enhanced and this is reflected in the high loadings on Component 1 and low (negative) loadings on the precipitation component--Component 5 (Fig. 4). A low-average and low-range in turbidity is also a significant form giving influence, particularly in the winter months, as shown by Component 3. From this description of Solar Climate 2 and a knowledge of the region's dominant physiography, one would not expect this regime to extend to the coast. Since there are no coastal stations in the Northwest, however, this extrapolation was made by SYMAP even though it's not correct----climatically.The above problems in Region 2"s spatial expression notwithstanding, its solar climate can be described as an interior, continental-type that experiences seasonal extremes of radiation receipt, temperature and precipitation. Not coincidently, Region 2 consists of stations in rainshadows of the coast ranges and the northern Rockies. In Region 3, the mountainous physiography exerts highly variable influences on solar radiation making interpretations difficult. Climatic influences that generally grade latitudinally but not longitudinally appear to be important in delineating the bound-

Solar climates of the contern~gno~usUnited States aries--judging from the loading maps~ Stations in this region include three California stations and several high altitude stations ranging from Reno, Nevada (~1350m) to Laramie, Wyoming (~2150m). Loadings on Component 1 indicate a moderate to high seasonality. This is due, in part, to the dry summers characteristic of California's Mediterranean climate in the western part of the region. Radiation receipt is greater in summer and less in winter than would be expected, considering the region's mid-latitude location. Somewhat unexpectedly, the magnitudes of radiation received by the mountain stations were strongly covaried with the high values of radiation observed at the California stations. This can be explained, in part, by the reduction in the optical air mass which results in a greater instantaneous radiation receipt at higher elevations. The diurnally more variable summer atmospheric conditions of the Rocky Mountain stations, compared with the California stations, are thereby offset as this analysis is based on integrated daily totals. Component 4 also indicates that much of central California and the Rockies are generally not affected by mid-latitude cyclones, particularly in summet, but they are subject more to local influences. Intermediate loadings on Component 5, which reflect a less regular precipitation regime than might be expected in higher loading areas, tend to further verify this supposition. Seasonal fluctuations too are lessened, compared with the climate just to the north. Region 4, which encompasses most of the Southwest, is one of the most coherent and distinct solar climatic regions that emerged from the analysis. An examination of the dendrogram (Fig. 2) shows that stations belonging to this regime are grouped at a low level of fusion and remain a distinct cluster until very late in the agglomerative process. Perturbations in solar radiation are infrequent and of low range, owing to clear atmospheric conditions throughout most of the year, and the seasonal range is diminished because of the low latitudes. A map of mean daily radiation receipt also shows that this region receives the largest annual total of solar radiation in the United States (Fig. 4g This, of course, contributes to its distinctive character. Seasonality of radiation receipt for Region 5 varies from high in the north to moderate in the south. Summer is typified by periods of cloudless skies and perturbations of high range, as exhibited in the loadings on Component 2. An accentuated annual seasonality in precipitation producing clouds and therefore in the atmospheric attenuation of solar radiation is shown by the loadings on Component 5. Extratropical cyclones also begin to show their effects during the winter months although the passage of these storms is less frequent than further to the east. Winters are considerably cloudy causing both a low winter variance and mean. A consistent decrease in the average magnitude of solar radiation receipt as one proceeds froth the Southwest to the Southeast marks Region 6 as

301

transitional, seasonality is low and this is expressed in the moderate loadings on Component 1 and by the low amplitudes of the stations' 5-yr solar curves. Highly variable winters are caused by the alternative presence of cloudy Gulf air and dry desert air. Stations in this region load highly on Component 5 indicaring only minor variation in the patterns of precipitation producing clouds, and their effects on solar radiation, throughout the year. The seasonality in turbidity too becomes important as one progresses from the western to eastern reaches of the region. Despite the fact that this is a transitional region, in terms of average radiation received, it is coherent in terms of the pattern of solar variability. Region 7, encircling many of the Great Lakes states, lies just north of the major path of mid-latitude cyclonic storm systems and these disturbances produce a characteristic pattern of global radiation receipt at the surface. The spatial intensity of this influence is shown on the map of Component 4. Differences between the number of summer and winter days with precipitation are significantly dissimilar to New England and Texas. Moreover, a recurring pattern of summer cloudless periods, expressed in loadings on Component 2, identifies this region as more similar to its western neighbors than New England. In addition, turbidity is highly seasonal, i.e. greatest in summer and nearly absent in winter, while the intensity of the effect of days having precipitation-producing clouds is spatially described by the loadings on Component 5. A large annual and seasonal variability seem to characterize the Northeast--Region 8. At Portland, Maine,; for example; an extensive cloudless and therefore low perturbation period soon after the summer solstice in 1971 is not apparent at all in 1972. Variability during a given year is also large. Daily totals during the high-sun period, for instance, may range from under 4 MJ/m z to over 30 MJ/m z. Low-sun perturbations have a much smaller range but occur frequently. Region 8, as evidenced by the component loadings maps, reveals a most distinct set of variations in global radiation (Fig. 4). Concomitantly, the dendrogram (Fig. 2) describes this regime as the most coherent solar climate in the United States, i.e. it remained a distinct cluster until a level of fusion on the order of 10 times greater than the original formgiving fusion was reached. Region 8 loads moderately on the seasonality component, for such a high latitude regime, owing to a relatively large year-to-year variation in the amplitudes of the annual curves. Moreover, a large range in variability during summer is described by the loadings on Component 2. Passages of mid-latitude cyclones are frequent (Component 4), and they accentuate winter variability in global radiation. The precipitation producing cloud regime is essentially non-seasonal, however, as indicated by the high ioadings on Component 5. New England experiences diverse combinations of atmospheric conditions that paradoxically make it climatically coherent.

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An outstanding feature of Region 9 is the turbidity regime. The frequent occurrence of maritime tropical air in summer, and associated high turbidity, diminishes summer radiation to an average daily maximum of about 30 MJ/m 2. Extended periods of high magnitude receipt may occur in summer, but frequent perturbations still exist. Winter values, on the other hand, reflect general trends displayed by the other climates. That is, an increase in the magnitude of winter radiation is accompanied by an increase in the range of perturbations. The magnitude of Component 1 identifies Region 9 as having a moderate seasonal range. Perturbations during summer are reduced. Air from the Gulf of Mexico serves to push mid-latitude cyclones somewhat to the North, making their influence insignificant except in the northern part of the region. Low latitudes and frequent cloud cover, as expressed in the low amplitudes of the annual curves for Apalachicola, Florida, indicate the relatively small influence of seasonality on Region 10. At the same time, a wide day-to-day range throughout the year is a reflection of the highly variable cloud cover that occurs in all seasons. Owing to a paucity of systematic variation in the station records of this climate, Region 10 is least well explained by the five-component solution (Fig. 1). Its definition, therefore, is based on (1) mean values of radiation receipt which are the highest outside of the Southwest and (2) low to moderate loadings on the components. 8. PLANNING IMPLICATIONS

An objectively defined univariate classification of global radiation promises to be of utility in two important areas in addition to its pedagogic value and general information content. In the area of solar dwelling and/or collector design, if "good" general designs are to be developed, arcfiitects and planners must first have an accurate picture of the number and character of solar climatic regimes in the United States as well as their spatial extent [2]. The number of these solar regimes has been determined to be 10 and the regionalization (Fig. 3) describes their spatial form. Each region has also been characterized by subjective evaluations made from the 5-yr solar records, the five components, the station means and a variety of other sources. Because it would be difficult, for example, to design a solar collecting system using only the verbal description of a climate, however, station records which epitomize each solar climate were objectively selected according to a simple criterion described by Willmott [6]. Corresponding to the climatic regions in ascending order these stations are: Seattle-Tacoma Airport, Washington; Medford, Oregon; Grand Junction, Colorado; Tucson, Arizona; Bismarck, North Dakota; Oklahoma City, Oklahoma; Lemont, Illinois; Burlington, Vermont; Oak Ridge, Tennessee; Apalachicola, Florida. It is suggested, in other words, that the solar

radiation records from these stations be used to design solar collectors and dwellings for locales that fall within the corresponding solar climatic regions. Even though there are losses of information and errors associated with this recommended design procedure, the system is thought to be far superior to the past practice of recommending dwelling types, for instance, on the basis of climatic classifications of inappropriate variables. A recent government publication [14-], which was discussed by Willmott [2], illustrates the problem as well as a need for renewed research in the classification of our climatic energy resources. Of comparable planning import, accurate information about the spatial distribution of climatic regions and their representative stations should aid NOAA and other agencies concerned with data collection, management and dissemination in reducing attendant expenses by deleting unnecessary stations from the network. Not only should unnecessary stations be removed from the network but a few new stations should be added so that all the significant sources of solar variation are adequately sampled. Even with some additions, however, our results suggest that a substantial reduction in the number of solar monitoring stations, accompanied by a small loss of information, is possible. Selection of stations which would form this network should have (and have had) a higher priority in the ongoing formulation of our "'new" solar monitoring network. The results reported on here do not begin to answer enough of the questions but it is hoped that they will help point the way to more and better research in this area. 9. SUMMARY AND CONCLUSIONS

Ten solar climates in the conterminous United States have been identified, described and mapped. Data for the study were 5 yr of daily total global radiation observed at 60 stations from 1970 to 1974. These data were digitally summarized by a P-mode principal components analysis of a covariance matrix and an optimized Ward's grouping of five principal components and the station means. Most of the computational methods used follow steps described by Willmott [6] and Vernon [12]. It is hoped that these results will be of value both as an example of taxometric climatic classification and as a source of information for planners and teachers concerned with the temporal vicissitudes and spatial variations of solar energy. Moreover, these results are thought to point to a means by which redundant and expensive data collection may be substantially reduced.

Acknowledgements

Support from the Computing Center

and the Center for Climatic Research at the University of Delaware is acknowledged. We would also like to thank William D. Philpot (Marine Studies, Delaware) for critically reviewing an earlier draft of this paper. Any errors which have gone undetected, however, are entirely the responsibility of the authors.

Solar climates of the conterminous United States REFERENCES

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