USGS Science⌕ Search

SEARCH · USGS Science

Results for “Journal of Applied Meteorology and Climatology”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

11 recordsLinked to original sources
USGS2016

Climate change implications for tropical islands: Interpolating and interpreting statistically downscaled GCM projections for management and planning

The potential ecological and economic effects of climate change for tropical islands were studied using output from 12 statistically downscaled general circulation models (GCMs) taking Puerto Rico as a test case. Two model selection/model averaging strategies were used: the average of all available GCMs and the average of the models that are able to reproduce the observed large-scale dynamics that control precipitation over the Caribbean. Five island-wide and multidecadal averages of daily precipitation and temperature were estimated by way of a climatology-informed interpolation of the site-specific downscaled climate model output. Annual cooling degree-days (CDD) were calculated as a proxy index for air-conditioning energy demand, and two measures of annual no-rainfall days were used as drought indices. Holdridge life zone classification was used to map the possible ecological effects of climate change. Precipitation is predicted to decline in both model ensembles, but the decrease was more severe in the “regionally consistent” models. The precipitation declines cause gradual and linear increases in drought intensity and extremes. The warming from the 1960–90 period to the 2071–99 period was 4.6°–9°C depending on the global emission scenarios and location. This warming may cause increases in CDD, and consequently increasing energy demands. Life zones may shift from wetter to drier zones with the possibility of losing most, if not all, of the subtropical rain forests and extinction risks to rain forest specialists or obligates.

Puerto Rico↗
USGS1993

The use of NOAA AVHRR data for assessment of the urban heat sland effect

A vegetation index and a radiative surface temperature were derived from satellite data acquired at approximately 1330 LST for each of 37 cities and for their respective nearby rural regions from 28 June through 8 August 1991. Urban–rural differences for the vegetation index and the surface temperatures were computed and then compared to observed urban–rural differences in minimum air temperatures. The purpose of these comparisons was to evaluate the use of satellite data to assess the influence of the urban environment on observed minimum air temperatures (the urban heat island effect). The temporal consistency of the data, from daily data to weekly, biweekly, and monthly intervals, was also evaluated. The satellite-derived normalized difference (ND) vegetation-index data, sampled over urban and rural regions composed of a variety of land surface environments, were linearly related to the difference in observed urban and rural minimum temperatures. The relationship between the ND index and observed differences in minimum temperature was improved when analyses were restricted by elevation differences between the sample locations and when biweekly or monthly intervals were utilized. The difference in the ND index between urban and rural regions appears to be an indicator of the difference in surface properties (evaporation and heat storage capacity) between the two environments that are responsible for differences in urban and rural minimum temperatures. The urban and rural differences in the ND index explain a greater amount of the variation observed in minimum temperature differences than past analyses that utilized urban population data. The use of satellite data may contribute to a globally consistent method for analysis of urban heat island bias.

Journal of Applied Meteorology and Climatology↗
USGS2015

Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern Great Plains using the vegetation drought response index (VegDRI)

The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of record-breaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico’s history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing.

Journal of Applied Meteorology and Climatology↗
USGS2015

Characterizing Congo Basin rainfall and climate using Tropical Rainfall Measuring Mission (TRMM) satellite data and limited rain gauge ground observations

Quantitative understanding of Congo River basin hydrological behavior is poor because of the basin’s limited hydrometeorological observation network. In cases such as the Congo basin where ground data are scarce, satellite-based estimates of rainfall, such as those from the joint NASA/JAXA Tropical Rainfall Measuring Mission (TRMM), can be used to quantify rainfall patterns. This study tests and reports the use of limited rainfall gauge data within the Democratic Republic of Congo (DRC) to recalibrate a TRMM science product (TRMM 3B42, version 6) in characterizing precipitation and climate in the Congo basin. Rainfall estimates from TRMM 3B42, version 6, are compared and adjusted using ground precipitation data from 12 DRC meteorological stations from 1998 to 2007. Adjustment is achieved on a monthly scale by using a regression-tree algorithm. The output is a new, basin-specific estimate of monthly and annual rainfall and climate types across the Congo basin. This new product and the latest version-7 TRMM 3B43 science product are validated by using an independent long-term dataset of historical isohyets. Standard errors of the estimate, root-mean-square errors, and regression coefficients r were slightly and uniformly better with the recalibration from this study when compared with the 3B43 product (mean monthly standard errors of 31 and 40 mm of precipitation and mean r 2 of 0.85 and 0.82, respectively), but the 3B43 product was slightly better in terms of bias estimation (1.02 and 1.00). Despite reasonable doubts that have been expressed in studies of other tropical regions, within the Congo basin the TRMM science product (3B43) performed in a manner that is comparable to the performance of the recalibrated product that is described in this study.

Journal of Applied Meteorology and Climatology↗
USGS2016

The sensitivity of WRF downscaled precipitation in Puerto Rico to cumulus parameterization and interior grid nudging

The sensitivity of the Weather Research and Forecasting Model (WRF) simulated precipitation over Puerto Rico is evaluated using multiple combinations of cumulus parameterization (CP) schemes and interior grid nudging. NCEP-DOE AMIP-II reanalysis (R-2) is downscaled to 2- km horizontal grid spacing with both convective permitting simulations (CP active only in the 49 middle and outer domains) and CP schemes active in all domains. The results generally show lower simulated precipitation amounts compared to the observations, regardless of WRF configuration. However, activating the CP schemes in the inner domain improves the annual cycle, intensity, and placement of rainfall compared to the convective permitting simulations. Furthermore, the use of interior grid nudging techniques in the outer domains improves the placement and intensity of rainfall in the inner domain. Incorporating a CP scheme at convective permitting scales (< 4 km) and grid nudging at non-convective permitting scales (> 4 km) improves the island average correlation of precipitation by 0.05 to 0.2 and reduces the island average RMSE by up to 40 mm on average over relying on the explicit microphysics at convective permitting scales with grid nudging. Projected changes in summer precipitation between 2040-2042 and 1985-1987 using WRF to downscale CCSM4 ranges from a 2.6 mm average increase to 81.9 mm average decrease, depending on the choice of CP scheme. The differences are only associated with differences between WRF configurations, which indicates the importance of CP scheme for projected precipitation change as well as historical accuracy.

Puerto Rico↗
USGS2017

Characterizing sources of uncertainty from global climate models and downscaling techniques

In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (> 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Journal of Applied Meteorology and Climatology↗
USGS2017

Examining the value of global seasonal reference evapotranspiration forecasts to support FEWS NET’s food insecurity outlooks

The Famine Early Warning Systems Network (FEWS NET) team provides food insecurity outlooks for several developing countries in Africa, Central Asia, and Central America. This study describes development of a new global reference evapotranspiration (ETo) seasonal reforecast and skill evaluation with a particular emphasis on the potential use of this dataset by the FEWS NET to support food insecurity early warning. The ETo reforecasts span the 1982-2009 period and are calculated following ASCE’s formulation of Penman-Monteith method driven by seasonal climate forecasts of monthly mean temperature, humidity, wind speed, and solar radiation from NCEP’s CFSv2 and NASA’s GEOS-5 models. The skill evaluation using deterministic and probabilistic scores, focuses on the December-February (DJF), March-May (MAM), June-August (JJA) and September-November (SON) seasons. The results indicate that ETo forecasts are a promising tool for early warning of drought and food insecurity. Globally, the regions where forecasts are most skillful (correlation >0.35 at lead-2) include Western U.S., northern parts of South America, parts of Sahel region and Southern Africa. The FEWS NET regions where forecasts are most skillful (correlation >0.35 at lead-3) include Northern Sub-Saharan Africa (DJF, dry season), Central America (DJF, dry season), parts of East Africa (JJA, wet Season), Southern Africa (JJA, dry season), and Central Asia (MAM, wet season). A case study over parts of East Africa for the JJA season shows that ETo forecasts in combination with the precipitation forecasts could have provided early warning of recent severe drought events (e.g., 2002, 2004, 2009) that contributed to substantial food insecurity in the region.

Journal of Applied Meteorology and Climatology↗
USGS2018

Assessment of Alaska rain-on-snow events using dynamical downscaling

The ice formed by cold-season rainfall or rain on snow (ROS) has striking impacts on the economy and ecology of Alaska. An understanding of the atmospheric drivers of ROS events is required to better predict them and plan for environmental change. The spatially/temporally sparse network of stations in Alaska makes studying such events challenging, and gridded reanalysis or remote sensing products are necessary to fill the gaps. Recently developed dynamically downscaled climate data provide a new suite of high-resolution variables for investigating historical and projected ROS events across all of Alaska from 1979 to 2100. The dynamically downscaled reanalysis data of ERA-Interim replicated the seasonal patterns of ROS events but tended to produce more rain events than in station observations. However, dynamical downscaling reduced the bias toward more rain events in the coarse reanalysis. ROS occurred most frequently over southwestern and southern coastal regions. Extreme events with the heaviest rainfall generally coincided with anomalous high pressure centered to the south/southeast of the locations receiving the event and warm-air advection from the resulting southwesterly wind flow. ROS events were projected to increase in frequency overall and for extremes across most of the region but were expected to decline over southwestern/southern Alaska. Increases in frequency were projected as a result of more frequent winter rainfall, but the number of ROS events may ultimately decline in some areas as a result of temperatures rising above the freezing threshold. These projected changes in ROS can significantly affect wildlife, vegetation, and human activities across the Alaska landscape.

Alaska↗
USGS2023

Limited role of absolute humidity in intraurban heat variability

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.

Journal of Applied Meteorology and Climatology↗
USGS1997

Digital-map grids of mean-annual precipitation for 1961-90, and generalized skew coefficients of annual maximum streamflow for Oklahoma

This digital report contains two digital-map grids of data that were used to develop peak-flow regression equations in Tortorelli, 1997, 'Techniques for estimating peak-streamflow frequency for unregulated streams and streams regulated by small floodwater retarding structures in Oklahoma,' U.S. Geological Survey Water-Resources Investigations Report 97-4202. One data set is a grid of mean annual precipitation, in inches, based on the period 1961-90, for Oklahoma. The data set was derived from the PRISM (Parameter-elevation Regressions on Independent Slopes Model) mean annual precipitation grid for the United States, developed by Daly, Neilson, and Phillips (1994, 'A statistical-topographic model for mapping climatological precipitation over mountainous terrain:' Journal of Applied Meteorology, v. 33, no. 2, p. 140-158). The second data set is a grid of generalized skew coefficients of logarithms of annual maximum streamflow for Oklahoma streams less than or equal to 2,510 square miles in drainage area. This grid of skew coefficients is taken from figure 11 of Tortorelli and Bergman, 1985, 'Techniques for estimating flood peak discharges for unregulated streams and streams regulated by small floodwater retarding structures in Oklahoma,' U.S. Geological Survey Water-Resources Investigations Report 84-4358. To save disk space, the skew coefficient values have been multiplied by 100 and rounded to integers with two significant digits. The data sets are provided in an ASCII grid format.

Oklahoma↗
USGS2008

Distributed energy balance modeling of South Cascade Glacier, Washington and assessment of model uncertainty

We have developed a physically based, distributed surface energy balance model to simulate glacier mass balance under meteorological and climatological forcing. Here we apply the model to estimate summer ablation on South Cascade Glacier, Washington, for the 2004 and 2005 mass balance seasons. To arrive at optimal mass balance simulations, we investigate and quantify model uncertainty associated with selecting from a range of physical parameter values that are not commonly measured in glaciological mass balance field studies. We optimize the performance of the model by varying values for atmospheric transmissivity, the albedo of surrounding topography, precipitation-elevation lapse rate, surface roughness for turbulent exchange of momentum, and snow albedo aging coefficient. Of these the snow aging parameter and precipitation lapse rates have the greatest influence on the modeled ablation. We examined model sensitivity to varying parameters by performing an additional 103 realizations with parameters randomly chosen over a ??5% range centered about the optimum values. The best fit suite of model parameters yielded a net balance of -1.69??0.38 m water equivalent (WE) for the 2004 water year and -2.10??0.30 m WE up to 11 September 2005. The 2004 result is within 3% of the measured value. These simulations account for 91% and 93% of the variance in measured ablation for the respective years. Copyright 2008 by the American Geophysical Union.

Journal of Geophysical Research F: Earth Surface↗