Search Results
You are looking at 1 - 10 of 70 items for
- Author or Editor: Thomas R. Karl x
- Refine by Access: All Content x
Abstract
Statistical forecast equations have been developed for the Greater St. Louis, Missouri, area using Model Output Statistics (MOS) derived from the National Meteorological Center's Limited-Area Fine Mesh (LFM) model. They are used to forecast both the probability of ozone concentrations exceeding the 1971 National Ambient Air Quality Standard and the daily 1 h maximum. Predictions extend out to two days (48 h).
The application of MOS to forecasts of maximum O3 concentrations lead to skillful [better than chance, persistence or climatology (seasonality)] 24 and 48 h objective predictions. The application of MOS to probability statements about O3 concentrations also resulted in reasonable success for 24 h and 48 h probability forecasts. These forecasts appear sufficiently successful to warrant consideration of the development of other equations for large metropolitan area where O3 concentrations commonly exceed the standard.
Abstract
Statistical forecast equations have been developed for the Greater St. Louis, Missouri, area using Model Output Statistics (MOS) derived from the National Meteorological Center's Limited-Area Fine Mesh (LFM) model. They are used to forecast both the probability of ozone concentrations exceeding the 1971 National Ambient Air Quality Standard and the daily 1 h maximum. Predictions extend out to two days (48 h).
The application of MOS to forecasts of maximum O3 concentrations lead to skillful [better than chance, persistence or climatology (seasonality)] 24 and 48 h objective predictions. The application of MOS to probability statements about O3 concentrations also resulted in reasonable success for 24 h and 48 h probability forecasts. These forecasts appear sufficiently successful to warrant consideration of the development of other equations for large metropolitan area where O3 concentrations commonly exceed the standard.
Abstract
The water content parameter and the moisture anomaly index, both derived from the Palmer Drought Severity model, were correlated against subsequent mean monthly and seasonal (three-month means) temperatures for 344 climatic divisions in the United States (1931–83). During spring and summer, Monte Carlo field significance tests demonstrate that the correlation fields produced from the soil moisture parameters are significantly larger than those derived using persistence of monthly and seasonal temperature anomalies. The areas of the United States with enhanced soil moisture parameterization–temperature correlations tend to be confined to the interior. The average reduction of the standard error of estimate (root-mean-square error) is 0.15°C for seasonal forecasts made at the end of April and May over inland, nonarid climate divisions. The reduction of the standard error is less for monthly forecasts (∼0.05°C) during the April through July forecast time periods. The areas with the greatest reduction in error favor the southern portions of the United States during early spring, but in late spring and summer they are also found in the central and northern states.
The empirical relationships found between soil moisture parameters and subsequent monthly and seasonal temperature suggest that soil moisture is important on a local scale, not only in a diagnostic mode, but also in a prognostic sense. Since the spring and early summer are times when the persistence of temperature anomalies is not very effective as a prediction tool, the existence of a demonstrable increase in predictability of monthly and seasonal temperatures using soil moisture indices, easily calculated on an operational basis, suggests that another objective long-range forecast aid is available for immediate use.
Further increases of the correlations of soil moisture with subsequent monthly and seasonal mean temperature may come from improvements in the water balance computations which are part of the Palmer model, i.e., estimation of evapotranspiration, the treatment of runoff, the inclusion of snow cover, the inclusion of irrigation estimates, or from other models, or preferably from a network of soil moisture measurements.
With respect to the Palmer water budget evapotranspiration calculations, sensitivity studies of the soil moisture parameterizations were performed using a fixed annual cycle of evapotranspiration—no year-to-year variations of the annual cycle. Statistically significant, but not drastic, degradations of the correlations of parameterized soil moisture with subsequent seasonal and monthly mean temperature were noted. The largest increase of forecast error occurred during the seasonal forecast period. This suggests that improved estimates of evapotranspiration in the water balance equation may be especially important for long (seasonal) forecasting periods during late spring and summer, but dramatic improvements in forecast skill may be difficult to achieve.
Abstract
The water content parameter and the moisture anomaly index, both derived from the Palmer Drought Severity model, were correlated against subsequent mean monthly and seasonal (three-month means) temperatures for 344 climatic divisions in the United States (1931–83). During spring and summer, Monte Carlo field significance tests demonstrate that the correlation fields produced from the soil moisture parameters are significantly larger than those derived using persistence of monthly and seasonal temperature anomalies. The areas of the United States with enhanced soil moisture parameterization–temperature correlations tend to be confined to the interior. The average reduction of the standard error of estimate (root-mean-square error) is 0.15°C for seasonal forecasts made at the end of April and May over inland, nonarid climate divisions. The reduction of the standard error is less for monthly forecasts (∼0.05°C) during the April through July forecast time periods. The areas with the greatest reduction in error favor the southern portions of the United States during early spring, but in late spring and summer they are also found in the central and northern states.
The empirical relationships found between soil moisture parameters and subsequent monthly and seasonal temperature suggest that soil moisture is important on a local scale, not only in a diagnostic mode, but also in a prognostic sense. Since the spring and early summer are times when the persistence of temperature anomalies is not very effective as a prediction tool, the existence of a demonstrable increase in predictability of monthly and seasonal temperatures using soil moisture indices, easily calculated on an operational basis, suggests that another objective long-range forecast aid is available for immediate use.
Further increases of the correlations of soil moisture with subsequent monthly and seasonal mean temperature may come from improvements in the water balance computations which are part of the Palmer model, i.e., estimation of evapotranspiration, the treatment of runoff, the inclusion of snow cover, the inclusion of irrigation estimates, or from other models, or preferably from a network of soil moisture measurements.
With respect to the Palmer water budget evapotranspiration calculations, sensitivity studies of the soil moisture parameterizations were performed using a fixed annual cycle of evapotranspiration—no year-to-year variations of the annual cycle. Statistically significant, but not drastic, degradations of the correlations of parameterized soil moisture with subsequent seasonal and monthly mean temperature were noted. The largest increase of forecast error occurred during the seasonal forecast period. This suggests that improved estimates of evapotranspiration in the water balance equation may be especially important for long (seasonal) forecasting periods during late spring and summer, but dramatic improvements in forecast skill may be difficult to achieve.
Abstract
By using the Palmer Drought Severity Index (PDSI) as calculated from state averages of temperature and precipitation and from numerous single station analyses, it has been demonstrated that droughts (as defined by the PDSI) persist longer in the interior of the United States than in areas farther east or west. The question arises whether this is merely an artifact of the PDSI calculations, or whether there is actually more persistence of abnormally dry (wet) weather in the interior.
The sensitivity of the PDSI was tested in relation to changes of derived and prescribed parameters included in the PDSI calculations in order to determine their effect on the spatial characteristics of drought duration. The sensitivity tests indicated a negligible effect. Contingency tables which use the PDSI as the predictor for the following one-month, six-month and 12-month precipitation anomalies (and also anomalies of precipitation minus potential evapotranspiration) however, are generally characterized by significantly greater skill in the interior portions of the United States, confirming the nation that spells of abnormally dry or wet weather do have more persistence in the Rocky Mountain and High Plains states than states farther east or west. Unfortunately, the forecasts derived from the “operational” PDSIs were not a significant improvement from what would have been obtained by using precipitation persistence forecasts.
Abstract
By using the Palmer Drought Severity Index (PDSI) as calculated from state averages of temperature and precipitation and from numerous single station analyses, it has been demonstrated that droughts (as defined by the PDSI) persist longer in the interior of the United States than in areas farther east or west. The question arises whether this is merely an artifact of the PDSI calculations, or whether there is actually more persistence of abnormally dry (wet) weather in the interior.
The sensitivity of the PDSI was tested in relation to changes of derived and prescribed parameters included in the PDSI calculations in order to determine their effect on the spatial characteristics of drought duration. The sensitivity tests indicated a negligible effect. Contingency tables which use the PDSI as the predictor for the following one-month, six-month and 12-month precipitation anomalies (and also anomalies of precipitation minus potential evapotranspiration) however, are generally characterized by significantly greater skill in the interior portions of the United States, confirming the nation that spells of abnormally dry or wet weather do have more persistence in the Rocky Mountain and High Plains states than states farther east or west. Unfortunately, the forecasts derived from the “operational” PDSIs were not a significant improvement from what would have been obtained by using precipitation persistence forecasts.
Abstract
The ratio of the probability of at least one extremely cold or warm month (standardized departures ≥ 1.282 or ≤ −1.282) in a season with near-normal mean temperatures (standardized departures ≤ 0.524 but ≥−0.524) to the probability of such an event in abnormal seasons has been calculated using statewide average monthly temperatures (1895–1983) across the United States. Values of this ratio, termed the “ratio of variability” (RV), near one reflect nearly an equal probability of one or more extreme months in both near-normal and abnormal seasons, while values near zero indicate little chance of an extreme month in a near-normal season. The values of RV vary with geographic location and the time of year in a systematic predictable manner. The magnitude of RV is greater during the transition seasons than during either summer or winter. The gradients of RV are relatively flat in the autumn, but comparatively sharp in spring with a maximum in the east and central United States and a minimum in the west. In the summer the largest values of RV are found along the northern tier of states and along the mid-Atlantic coastal states, but during winter the central portions of the United States and portions of the northeast have the largest values of RV.
The two factors responsible for the seasonal changes and spatial gradients of RV are the spatial and temporal changes of the month-to-month persistence (or lack of persistence) of unusually cold or warm weather and the unequal contribution of variances within a season by each of the months in the season. This is demonstrated using Monte Carlo simulations of an autoregressive model. Users of seasonal average temperatures, whether in a forecast or hindcast mode, whose operations are sensitive to persistent unusually cold or warm temperatures within a season with near-normal temperatures, should be cognizant of the spatial and temporal changes of RV.
Abstract
The ratio of the probability of at least one extremely cold or warm month (standardized departures ≥ 1.282 or ≤ −1.282) in a season with near-normal mean temperatures (standardized departures ≤ 0.524 but ≥−0.524) to the probability of such an event in abnormal seasons has been calculated using statewide average monthly temperatures (1895–1983) across the United States. Values of this ratio, termed the “ratio of variability” (RV), near one reflect nearly an equal probability of one or more extreme months in both near-normal and abnormal seasons, while values near zero indicate little chance of an extreme month in a near-normal season. The values of RV vary with geographic location and the time of year in a systematic predictable manner. The magnitude of RV is greater during the transition seasons than during either summer or winter. The gradients of RV are relatively flat in the autumn, but comparatively sharp in spring with a maximum in the east and central United States and a minimum in the west. In the summer the largest values of RV are found along the northern tier of states and along the mid-Atlantic coastal states, but during winter the central portions of the United States and portions of the northeast have the largest values of RV.
The two factors responsible for the seasonal changes and spatial gradients of RV are the spatial and temporal changes of the month-to-month persistence (or lack of persistence) of unusually cold or warm weather and the unequal contribution of variances within a season by each of the months in the season. This is demonstrated using Monte Carlo simulations of an autoregressive model. Users of seasonal average temperatures, whether in a forecast or hindcast mode, whose operations are sensitive to persistent unusually cold or warm temperatures within a season with near-normal temperatures, should be cognizant of the spatial and temporal changes of RV.
Abstract
The Palmer Drought Severity Index (PDSI) is routinely made available by NOAA for operational use, and it has also been calculated across the United States on a historical basis back to 1895 (Karl et al., 1983). Traditionally, the coefficients used in the calculation of the PDSI have been based on an anomalously hot and dry period across much of the United States (1931–60). By changing the base period used to calibrate the coefficients, the magnitude and the sign of the PDSI change significantly in many areas of the United States. Often the changes are larger than those that occur when the potential evapotranspiration is forced to a constant equal to the long-term monthly mean potential evapotranspiration. This sensitivity to base period calibration has important implications in the interpretation of operational or hindcast values of the PDSI for forest fire danger and other applications. The less frequently used Palmer moisture anomaly index (Z-index) is much less sensitive to changes in the calibration periods, and also has some desirable characteristics which may make it preferable to the PDSI for some agricultural and forest fire applications, i.e., it is more responsive to short-term moisture anomalies.
Abstract
The Palmer Drought Severity Index (PDSI) is routinely made available by NOAA for operational use, and it has also been calculated across the United States on a historical basis back to 1895 (Karl et al., 1983). Traditionally, the coefficients used in the calculation of the PDSI have been based on an anomalously hot and dry period across much of the United States (1931–60). By changing the base period used to calibrate the coefficients, the magnitude and the sign of the PDSI change significantly in many areas of the United States. Often the changes are larger than those that occur when the potential evapotranspiration is forced to a constant equal to the long-term monthly mean potential evapotranspiration. This sensitivity to base period calibration has important implications in the interpretation of operational or hindcast values of the PDSI for forest fire danger and other applications. The less frequently used Palmer moisture anomaly index (Z-index) is much less sensitive to changes in the calibration periods, and also has some desirable characteristics which may make it preferable to the PDSI for some agricultural and forest fire applications, i.e., it is more responsive to short-term moisture anomalies.
Abstract
At the National Climatic Data Center, two basic approaches to making homogeneity adjustments to climate data have been developed. The first is based on the use of metadata (station history files) and is used in the adjustments made to the U.S. Historical Climatology Network monthly dataset. The second approach is non-metadata based and was developed for use with the Global Historical Climatology Network dataset, since there are not extensive station history files for most stations in the dataset. In this paper the two methodologies are reviewed and the adjustments made using each are compared, then the results are discussed. Last, some brief guidelines on the limitations and uses of these data are provided.
Abstract
At the National Climatic Data Center, two basic approaches to making homogeneity adjustments to climate data have been developed. The first is based on the use of metadata (station history files) and is used in the adjustments made to the U.S. Historical Climatology Network monthly dataset. The second approach is non-metadata based and was developed for use with the Global Historical Climatology Network dataset, since there are not extensive station history files for most stations in the dataset. In this paper the two methodologies are reviewed and the adjustments made using each are compared, then the results are discussed. Last, some brief guidelines on the limitations and uses of these data are provided.
Abstract
Previous work has shown significant decreases of the diurnal temperature range (1941–80) across a network of 130 stations in the United States and Canada. In the present study, changes in monthly total precipitation at these same stations were related to the decrease in temperature range using various Monte Carlo. These tests indicate that factors other than those related to precipitation contributed to the decrease of daily temperature range. Further study of the mechanisms responsible for the decreased temperature range is warranted, based on these results. The decreased range may be one of the few pieces of evidence available in North America that is consistent with potential impacts of increased greenhouse gases and/or anthropogenic aerosols.
Abstract
Previous work has shown significant decreases of the diurnal temperature range (1941–80) across a network of 130 stations in the United States and Canada. In the present study, changes in monthly total precipitation at these same stations were related to the decrease in temperature range using various Monte Carlo. These tests indicate that factors other than those related to precipitation contributed to the decrease of daily temperature range. Further study of the mechanisms responsible for the decreased temperature range is warranted, based on these results. The decreased range may be one of the few pieces of evidence available in North America that is consistent with potential impacts of increased greenhouse gases and/or anthropogenic aerosols.
The dry and hot weather in the southeast United States during the first seven months of 1986 caused record drought. The agricultural and hydrological perspectives of this drought are examined via a climatological time series. Late nineteenth and twentieth century climate data from the most severely affected areas indicate that from an agricultural perspective the beginning and middle of the 1986 growing season was by far the worst on record. On the other hand, from a hydrological perspective the drought was not of sufficient duration to stand out as such an extreme anomaly. The 1986 drought is part of a change in recent years from the wet weather of the 1960s and much of the 1970s. At this time, there is no evidence to suggest that this change is anything more than another in a series of climate fluctuations typical throughout the climate records of many areas.
The dry and hot weather in the southeast United States during the first seven months of 1986 caused record drought. The agricultural and hydrological perspectives of this drought are examined via a climatological time series. Late nineteenth and twentieth century climate data from the most severely affected areas indicate that from an agricultural perspective the beginning and middle of the 1986 growing season was by far the worst on record. On the other hand, from a hydrological perspective the drought was not of sufficient duration to stand out as such an extreme anomaly. The 1986 drought is part of a change in recent years from the wet weather of the 1960s and much of the 1970s. At this time, there is no evidence to suggest that this change is anything more than another in a series of climate fluctuations typical throughout the climate records of many areas.
A data set derived from the United States Historical Climate Network has been compared to two global land-based temperature data sets that have been commonly cited in connection with the detection of the greenhouse effect and in other studies of climate change. Results indicate that in the United States the two global land-based temperature data sets have an urban bias between + 0.1°C and +0.4°C over the twentieth century (1901–84). This bias is as large or larger than the overall temperature trend in the United States during this time period, +0.16°C/84 yr. Temperature trends indicate an increasing temperature from the turn of the century to the 1930s but a decrease thereafter. By comparison, the global temperature trends during the same period are between +0.4°C/84 yr and +0.6°C/84 yr. At this time, we can only speculate on the magnitude of the urban bias in the global land-based data sets for other parts of the globe, but the magnitude of the bias in the United States compared to the overall temperature trend underscores the need for a thorough global study.
A data set derived from the United States Historical Climate Network has been compared to two global land-based temperature data sets that have been commonly cited in connection with the detection of the greenhouse effect and in other studies of climate change. Results indicate that in the United States the two global land-based temperature data sets have an urban bias between + 0.1°C and +0.4°C over the twentieth century (1901–84). This bias is as large or larger than the overall temperature trend in the United States during this time period, +0.16°C/84 yr. Temperature trends indicate an increasing temperature from the turn of the century to the 1930s but a decrease thereafter. By comparison, the global temperature trends during the same period are between +0.4°C/84 yr and +0.6°C/84 yr. At this time, we can only speculate on the magnitude of the urban bias in the global land-based data sets for other parts of the globe, but the magnitude of the bias in the United States compared to the overall temperature trend underscores the need for a thorough global study.
