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- Author or Editor: Kevin Gallo x
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Abstract
Adjustments to data observed at pairs of climate stations have been recommended to remove the biases introduced by differences between the stations in time of observation, temperature instrumentation, latitude, and elevation. A new network of climate stations, located in rural settings, permits comparisons of temperatures for several pairs of stations without two of the biases (time of observation and instrumentation). The daily, monthly, and annual minimum, maximum, and mean temperatures were compared for five pairs of stations included in the U.S. Climate Reference Network. Significant differences were found between the paired stations in the annual minimum, maximum, and mean temperatures for all five pairs of stations. Adjustments for latitude and elevation differences contributed to greater differences in mean annual temperature for four of the five stations. Lapse rates computed from the mean annual temperature differences between station pairs differed from a constant value, whether or not latitude adjustments were made to the data. The results suggest that microclimate influences on temperatures observed at nearby (horizontally and vertically) stations are potentially much greater than influences that might be due to latitude or elevation differences between the stations.
Abstract
Adjustments to data observed at pairs of climate stations have been recommended to remove the biases introduced by differences between the stations in time of observation, temperature instrumentation, latitude, and elevation. A new network of climate stations, located in rural settings, permits comparisons of temperatures for several pairs of stations without two of the biases (time of observation and instrumentation). The daily, monthly, and annual minimum, maximum, and mean temperatures were compared for five pairs of stations included in the U.S. Climate Reference Network. Significant differences were found between the paired stations in the annual minimum, maximum, and mean temperatures for all five pairs of stations. Adjustments for latitude and elevation differences contributed to greater differences in mean annual temperature for four of the five stations. Lapse rates computed from the mean annual temperature differences between station pairs differed from a constant value, whether or not latitude adjustments were made to the data. The results suggest that microclimate influences on temperatures observed at nearby (horizontally and vertically) stations are potentially much greater than influences that might be due to latitude or elevation differences between the stations.
Abstract
Satellite-derived observations of land surface temperature (LST) are being utilized in a growing number of land surface studies; however, these observations are generally obtained from optical sensors that exclude cloudy observations of the land surface. The impact of using only clear-sky observations of land surfaces on monthly and annual estimates of daytime LST over two U.S. Climate Reference Network (USCRN) sites was evaluated over five years with daily in situ LST observations available for all-sky (clear and cloudy) conditions. The in situ LST observations were obtained for the nominal daytime observations associated with the MODIS sensors on board the Terra and Aqua satellites and were identified as all-sky or clear-sky conditions by utilizing cloud information provided with the MODIS LST product. Both monthly/annual mean and monthly/annual maximum values of daytime LST were significantly different when only clear-sky values were utilized, in comparison with all-sky values. Monthly averaged differences between the mean clear- and all-sky daytime LST (dLST) values ranged from −0.1° ± 1.5°C for January to 5.6° ± 1.8°C for May. Annually averaged dLST values, over the five years of the study, were 2.58°C, and differences between the maximum values of clear- and all-sky daytime LST values were −1.03°C. Although significant differences between mean annual clear-sky and all-sky daytime LST values were more frequent than differences observed for the annual maximum daytime LST values, the results suggest that the exclusive use of either mean or maximum clear-sky daytime LST values is not advisable for applications in which the use of daytime all-sky LST values would be more applicable.
Abstract
Satellite-derived observations of land surface temperature (LST) are being utilized in a growing number of land surface studies; however, these observations are generally obtained from optical sensors that exclude cloudy observations of the land surface. The impact of using only clear-sky observations of land surfaces on monthly and annual estimates of daytime LST over two U.S. Climate Reference Network (USCRN) sites was evaluated over five years with daily in situ LST observations available for all-sky (clear and cloudy) conditions. The in situ LST observations were obtained for the nominal daytime observations associated with the MODIS sensors on board the Terra and Aqua satellites and were identified as all-sky or clear-sky conditions by utilizing cloud information provided with the MODIS LST product. Both monthly/annual mean and monthly/annual maximum values of daytime LST were significantly different when only clear-sky values were utilized, in comparison with all-sky values. Monthly averaged differences between the mean clear- and all-sky daytime LST (dLST) values ranged from −0.1° ± 1.5°C for January to 5.6° ± 1.8°C for May. Annually averaged dLST values, over the five years of the study, were 2.58°C, and differences between the maximum values of clear- and all-sky daytime LST values were −1.03°C. Although significant differences between mean annual clear-sky and all-sky daytime LST values were more frequent than differences observed for the annual maximum daytime LST values, the results suggest that the exclusive use of either mean or maximum clear-sky daytime LST values is not advisable for applications in which the use of daytime all-sky LST values would be more applicable.
Abstract
Monthly and seasonal relationships between urban–rural differences in minimum, maximum, and average temperatures measured at surface-based observation stations were compared to satellite-derived Advanced Very High Resolution Radiometer estimates of a normalized difference vegetation index (NDVI) and surface radiant temperature (T sfc). The relationships between surface- and satellite-derived variables were developed during 1989–91 and tested on data acquired during 1992–93. The urban–rural differences in air temperature were linearly related to urban–rural differences in the NDVI and T sfc. A statistically significant but relatively small (less than 40%) amount of the variation in these urban–rural differences in air temperature [the urban heat island (UHI) bias] was associated with variation in the urban–rural differences in NDVI and T sfc. A comparison of the satellite-based estimates of the UHI bias with population-based estimates of the UHI bias indicated similar levels of error. The use of satellite-derived data may contribute to a globally consistent method for analysis of the urban heat island bias.
Abstract
Monthly and seasonal relationships between urban–rural differences in minimum, maximum, and average temperatures measured at surface-based observation stations were compared to satellite-derived Advanced Very High Resolution Radiometer estimates of a normalized difference vegetation index (NDVI) and surface radiant temperature (T sfc). The relationships between surface- and satellite-derived variables were developed during 1989–91 and tested on data acquired during 1992–93. The urban–rural differences in air temperature were linearly related to urban–rural differences in the NDVI and T sfc. A statistically significant but relatively small (less than 40%) amount of the variation in these urban–rural differences in air temperature [the urban heat island (UHI) bias] was associated with variation in the urban–rural differences in NDVI and T sfc. A comparison of the satellite-based estimates of the UHI bias with population-based estimates of the UHI bias indicated similar levels of error. The use of satellite-derived data may contribute to a globally consistent method for analysis of the urban heat island bias.
Abstract
The United States Historical Climatology Network (HCN) serial temperature dataset is comprised of 1221 high-quality, long-term climate observing stations. The HCN dataset is available in several versions, one of which includes population-based temperature modifications to adjust urban temperatures for the “heat-island” effect. Unfortunately, the decennial population metadata file is not complete as missing values are present for 17.6% of the 12 210 population values associated with the 1221 individual stations during the 1900–90 interval. Retrospective grid-based populations, within a fixed distance of an HCN station, were estimated through the use of a gridded population density dataset and historically available U.S. Census county data. The grid-based populations for the HCN stations provide values derived from a consistent methodology compared to the current HCN populations that can vary as definitions of the area associated with a city change over time. The use of grid-based populations may minimally be appropriate to augment populations for HCN climate stations that lack any population data, and are recommended when consistent and complete population data are required. The recommended urban temperature adjustments based on the HCN and grid-based methods of estimating station population can be significantly different for individual stations within the HCN dataset.
Abstract
The United States Historical Climatology Network (HCN) serial temperature dataset is comprised of 1221 high-quality, long-term climate observing stations. The HCN dataset is available in several versions, one of which includes population-based temperature modifications to adjust urban temperatures for the “heat-island” effect. Unfortunately, the decennial population metadata file is not complete as missing values are present for 17.6% of the 12 210 population values associated with the 1221 individual stations during the 1900–90 interval. Retrospective grid-based populations, within a fixed distance of an HCN station, were estimated through the use of a gridded population density dataset and historically available U.S. Census county data. The grid-based populations for the HCN stations provide values derived from a consistent methodology compared to the current HCN populations that can vary as definitions of the area associated with a city change over time. The use of grid-based populations may minimally be appropriate to augment populations for HCN climate stations that lack any population data, and are recommended when consistent and complete population data are required. The recommended urban temperature adjustments based on the HCN and grid-based methods of estimating station population can be significantly different for individual stations within the HCN dataset.
Abstract
Clear and cloudy daytime comparisons of land surface temperature (LST) and air temperature (Tair) were made for 14 stations included in the U.S. Climate Reference Network (USCRN) of stations from observations made from 2003 through 2008. Generally, LST was greater than Tair for both the clear and cloudy conditions; however, the differences between LST and Tair were significantly less for the cloudy-sky conditions. In addition, the relationships between LST and Tair displayed less variability under the cloudy-sky conditions than under clear-sky conditions. Wind speed, time of the observation of Tair and LST, season, the occurrence of precipitation at the time of observation, and normalized difference vegetation index values were all considered in the evaluation of the relationship between Tair and LST. Mean differences between LST and Tair of less than 2°C were observed under cloudy conditions for the stations, as compared with a minimum difference of greater than 2°C (and as great as 7+°C) for the clear-sky conditions. Under cloudy conditions, Tair alone explained over 94%—and as great as 98%—of the variance observed in LST for the stations included in this analysis, as compared with a range of 81%–93% for clear-sky conditions. Because of the relatively homogeneous land surface characteristics encouraged in the immediate vicinity of USCRN stations, and potential regional differences in surface features that might influence the observed relationships, additional analyses of the relationships between LST and Tair for additional regions and land surface conditions are recommended.
Abstract
Clear and cloudy daytime comparisons of land surface temperature (LST) and air temperature (Tair) were made for 14 stations included in the U.S. Climate Reference Network (USCRN) of stations from observations made from 2003 through 2008. Generally, LST was greater than Tair for both the clear and cloudy conditions; however, the differences between LST and Tair were significantly less for the cloudy-sky conditions. In addition, the relationships between LST and Tair displayed less variability under the cloudy-sky conditions than under clear-sky conditions. Wind speed, time of the observation of Tair and LST, season, the occurrence of precipitation at the time of observation, and normalized difference vegetation index values were all considered in the evaluation of the relationship between Tair and LST. Mean differences between LST and Tair of less than 2°C were observed under cloudy conditions for the stations, as compared with a minimum difference of greater than 2°C (and as great as 7+°C) for the clear-sky conditions. Under cloudy conditions, Tair alone explained over 94%—and as great as 98%—of the variance observed in LST for the stations included in this analysis, as compared with a range of 81%–93% for clear-sky conditions. Because of the relatively homogeneous land surface characteristics encouraged in the immediate vicinity of USCRN stations, and potential regional differences in surface features that might influence the observed relationships, additional analyses of the relationships between LST and Tair for additional regions and land surface conditions are recommended.
Abstract
Several storms produced extensive hail damage over Iowa on 9 August 2009. The hail associated with these supercells was observed with radar data, reported by surface observers, and the resulting hail swaths were identified within satellite data. This study includes an initial assessment of cross validation of several radar-derived products and surface observations with satellite data for this storm event. Satellite-derived vegetation index data appear to be a useful product for cross validation of surface-based reports and radar-derived products associated with severe hail damage events. Satellite imagery acquired after the storm event indicated that decreased vegetation index values corresponded to locations of surface reported damage. The areal extent of decreased vegetation index values also corresponded to the spatial extent of the storms as characterized by analysis of radar data. While additional analyses are required and encouraged, these initial results suggest that satellite data of vegetated land surfaces are useful for cross validation of surface and radar-based observations of hail swaths and associated severe weather.
Abstract
Several storms produced extensive hail damage over Iowa on 9 August 2009. The hail associated with these supercells was observed with radar data, reported by surface observers, and the resulting hail swaths were identified within satellite data. This study includes an initial assessment of cross validation of several radar-derived products and surface observations with satellite data for this storm event. Satellite-derived vegetation index data appear to be a useful product for cross validation of surface-based reports and radar-derived products associated with severe hail damage events. Satellite imagery acquired after the storm event indicated that decreased vegetation index values corresponded to locations of surface reported damage. The areal extent of decreased vegetation index values also corresponded to the spatial extent of the storms as characterized by analysis of radar data. While additional analyses are required and encouraged, these initial results suggest that satellite data of vegetated land surfaces are useful for cross validation of surface and radar-based observations of hail swaths and associated severe weather.
Abstract
The diurnal temperature range (DTR) at weather observation stations that make up the U.S. Historical Climatology Network was evaluated with respect to the predominant land use/land cover associated with the stations within three radii intervals (100, 1000, and 10 000 m) of the stations. Those stations that were associated with predominantly rural land use/land cover (LULC) usually displayed the greatest observed DTR, whereas those associated with urban related land use or land cover displayed the least observed DTR. The results of this study suggest that significant differences in the climatological DTR were observed and could be attributed to the predominant LULC associated with the observation stations. The results also suggest that changes in the predominant LULC conditions, within as great as a 10 000 m radius of an observation station, could significantly influence the climatological DTR. Future changes in the predominant LULC associated with observation sites should be monitored similar to the current practice of monitoring changes in instruments or time of observation at the observations sites.
Abstract
The diurnal temperature range (DTR) at weather observation stations that make up the U.S. Historical Climatology Network was evaluated with respect to the predominant land use/land cover associated with the stations within three radii intervals (100, 1000, and 10 000 m) of the stations. Those stations that were associated with predominantly rural land use/land cover (LULC) usually displayed the greatest observed DTR, whereas those associated with urban related land use or land cover displayed the least observed DTR. The results of this study suggest that significant differences in the climatological DTR were observed and could be attributed to the predominant LULC associated with the observation stations. The results also suggest that changes in the predominant LULC conditions, within as great as a 10 000 m radius of an observation station, could significantly influence the climatological DTR. Future changes in the predominant LULC associated with observation sites should be monitored similar to the current practice of monitoring changes in instruments or time of observation at the observations sites.
Abstract
A subset of stations from the daily U.S. Historical Climatology Network (HCN) is used as a basis for a historical database of temperature extreme occurrence in the United States. The dataset focuses on daily temperature occurrences that exceed (fall below) the 90th (10th) percentiles of daily maximum and minimum temperature. Using a variety of techniques, the temperature extreme occurrence data are homogenized to account for nonclimatic shifts resulting from station relocations, changes in instrument type, and variations in the time of observations. Given the daily resolution of the extreme data, these potential sources of inhomogeneity require testing and adjustment using methods other than those conventionally used with mean temperature data. A data estimation technique, specific to extremes, is also used to produce serially complete exceedence records. Stations are also identified based on their current degree of urbanization using satellite observations. The dataset is intended to provide a research-quality source of temperature extreme data, analogous and complementary to the daily HCN dataset.
Two analyses are presented that illustrate the influence of adjustment. The change in temperature extreme occurrence with time reverses at between 15% and 20% of the HCN stations depending upon whether adjusted or unadjusted series is used. Changes in the distribution of extreme occurrences during drought and nondrought years are also shown to occur.
Abstract
A subset of stations from the daily U.S. Historical Climatology Network (HCN) is used as a basis for a historical database of temperature extreme occurrence in the United States. The dataset focuses on daily temperature occurrences that exceed (fall below) the 90th (10th) percentiles of daily maximum and minimum temperature. Using a variety of techniques, the temperature extreme occurrence data are homogenized to account for nonclimatic shifts resulting from station relocations, changes in instrument type, and variations in the time of observations. Given the daily resolution of the extreme data, these potential sources of inhomogeneity require testing and adjustment using methods other than those conventionally used with mean temperature data. A data estimation technique, specific to extremes, is also used to produce serially complete exceedence records. Stations are also identified based on their current degree of urbanization using satellite observations. The dataset is intended to provide a research-quality source of temperature extreme data, analogous and complementary to the daily HCN dataset.
Two analyses are presented that illustrate the influence of adjustment. The change in temperature extreme occurrence with time reverses at between 15% and 20% of the HCN stations depending upon whether adjusted or unadjusted series is used. Changes in the distribution of extreme occurrences during drought and nondrought years are also shown to occur.
ABSTRACT
Severe storm events that include hail and wind often cause widespread contiguous swaths of damage; however, their occurrence is typically documented at individual and disjointed locations. Satellite-derived products, such as the normalized difference vegetation index (NDVI), can provide a more spatially uniform look at the extent of these events, particularly in rural or remote areas. The utility of incorporating satellite-based products into the damage identification and documentation process was assessed through high-resolution ground surveys, which included digital photographs, to classify three levels of cropland damage for three severe hail/wind events occurring in the Great Plains during the summer of 2014. Pre- and postevent NDVI values at the photograph locations were calculated using surface reflectance values from the Moderate Resolution Imaging Spectroradiometer (MODIS) and grouped by damage severity level. In general, more severe crop damage displayed a lower NDVI in the postevent imagery compared to undamaged crops. Additionally, the difference in the median NDVI between the pre- and postevent images was statistically significant between the damage categories with similar trends observed across the three summertime events. Thus, satellite-derived products should be promoted as a valuable tool for the initial assessment of damage severity and extent to agricultural crops and should be integrated when possible into the current hazard documentation process as a supplement to the currently available point-based observations of storm damage.
ABSTRACT
Severe storm events that include hail and wind often cause widespread contiguous swaths of damage; however, their occurrence is typically documented at individual and disjointed locations. Satellite-derived products, such as the normalized difference vegetation index (NDVI), can provide a more spatially uniform look at the extent of these events, particularly in rural or remote areas. The utility of incorporating satellite-based products into the damage identification and documentation process was assessed through high-resolution ground surveys, which included digital photographs, to classify three levels of cropland damage for three severe hail/wind events occurring in the Great Plains during the summer of 2014. Pre- and postevent NDVI values at the photograph locations were calculated using surface reflectance values from the Moderate Resolution Imaging Spectroradiometer (MODIS) and grouped by damage severity level. In general, more severe crop damage displayed a lower NDVI in the postevent imagery compared to undamaged crops. Additionally, the difference in the median NDVI between the pre- and postevent images was statistically significant between the damage categories with similar trends observed across the three summertime events. Thus, satellite-derived products should be promoted as a valuable tool for the initial assessment of damage severity and extent to agricultural crops and should be integrated when possible into the current hazard documentation process as a supplement to the currently available point-based observations of storm damage.
