摘要 :
Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from November to March. It is associated with Mediterranean cyclones, and is sensitive to climate variability. Predicting the wet season precipi...
展开
Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from November to March. It is associated with Mediterranean cyclones, and is sensitive to climate variability. Predicting the wet season precipitation with a few months advance is highly valuable for water resource planning and climate-associated risk management in this semi-arid region. The regional water resource managements and climate-sensitive economic activities have relied on seasonal forecasts from global climate prediction centers. However due to their coarse resolutions, global seasonal forecasts lack regional and local scale information required by regional and local water resource managements. In this study, an analog statistical-downscaling algorithm, k-nearest neighbors (KNN), was introduced to bridge the gap between the coarse forecasts from global models and the needed fine-scale information for the Southeastern Mediterranean. The algorithm, driven by the NCEP Climate Forecast System (CFS) operational forecast and the NCEP/DOE reanalysis, provides monthly precipitations at 2-4 months of lead-time at 18 stations within the major regional hydrological basins. Large-scale predictors for KNN were objectively determined by the correlations between the station historic daily precipitation and variables in reanalysis and CFS reforecast. Besides a single deterministic forecast, this study constructed sixty ensemble members for probabilistic estimates. The KNN algorithm demonstrated its robustness when validated with NCEP/DOE reanalysis from 1981 to 2009 as hindcasts before applied to downscale CFS forecasts. The downscaled predictions show fine-scale information, such as station-to-station variability. The verification against observations shows improved skills of this downscaling utility relative to the CFS model. The KNN-based downscaling system has been in operation for the Israel Water Authority predicting precipitation and driving hydrologic models estimating river flow and aquifer charge for water supply.
收起
摘要 :
The National Water Model (NWM) was deployed by the National Oceanic and Atmospheric Administration to simulate operational forecasts of hydrologic states across the continental United States. This paper describes the geospatial ri...
展开
The National Water Model (NWM) was deployed by the National Oceanic and Atmospheric Administration to simulate operational forecasts of hydrologic states across the continental United States. This paper describes the geospatial river network (“hydro-fabric”), physics, and parameters of the NWM, elucidating the challenges of extrapolating parameters a large scale with limited observations. A set of regression-based channel geometry parameters are evaluated for a subset of the 2.7 million NWM reaches, and the riverine compound channel scheme is described. Based on the results from regional streamflow experiments within the broader NWM context, the compound channel reduced the root mean squared error by 2% and improved median Nash-Sutcliffe efficiency by 16% compared with a non-compound formulation. Peak event analysis from 910 peak flow events across 26 basins matched from the US Flash Flood Observation Database revealed that the mean timing error is 3 h lagged behind the observations. The routing time step was also tested, for 5-min (default, operational setting) and 1-h increments. The model was computationally stable and able to convey the flood peaks, although the hydrograph shape and peak timing were altered.
收起
摘要 :
We present and demonstrate a new methodology for retrieving liquid water path over land using satellite-based microwave observations. As input, the technique exploits Advanced Microwave Scanning Radiometer for EOS (AMSR-E) brightn...
展开
We present and demonstrate a new methodology for retrieving liquid water path over land using satellite-based microwave observations. As input, the technique exploits Advanced Microwave Scanning Radiometer for EOS (AMSR-E) brightness temperature polarization-difference signals at 37 and 89 GHz. Regression analysis performed on model simulations indicates that over variable atmospheric and surface conditions these polarization-difference signals can be simply parameterized in terms of the surface emissivity polarization-difference (Δ), surface temperature, liquid water path (LWP), and precipitable water vapor (PWV). By exploiting the weak frequency dependence of Δ, a simple expression is obtained which enables fast and direct (noniterative) retrievals of LWP. The new methodology is demonstrated and validated using several months of AMSR-E observations over (1) the Southern Great Plains (SGP) of the United States and (2) an area near Montreal, Canada, instrumented during the Alliance Icing Research Study II (AIRS II) field campaign. Comparisons are also made with MODIS LWP retrieval results for one scene over the SGP region. Retrieval results in clear-sky conditions indicate an uncertainty on the order of 0.06 mm, in agreement with theoretical estimates. In cloudy conditions, results using the new method are systematically smaller than results for both ground-based microwave radiometers and MODIS but are well correlated.
收起
摘要 :
Rural Wyoming is strongly impacted by hazardous weather along its road network. Interstate 80 (1-80) is a major highway that runs west to east along the southern part of the state, which is located in the Northern Rocky Mountain a...
展开
Rural Wyoming is strongly impacted by hazardous weather along its road network. Interstate 80 (1-80) is a major highway that runs west to east along the southern part of the state, which is located in the Northern Rocky Mountain area of the U.S. illustration 7. The entirety of 1-80 in Wyoming is over 1,800 meters in elevation, with a maximum of 2,633 meters.This elevation and Wyoming's continental, northerly climate lead to extreme weather along I-80 throughout the year.
收起
摘要 :
Atmospheric intraseasonal variability in the tropical Atlantic is analyzed using satellite winds, outgoing longwave radiation (OLR), and reanalysis products during 2000-08. The analyses focus on assessing the effects of dominant i...
展开
Atmospheric intraseasonal variability in the tropical Atlantic is analyzed using satellite winds, outgoing longwave radiation (OLR), and reanalysis products during 2000-08. The analyses focus on assessing the effects of dominant intraseasonal atmospheric convective processes, the Madden-Julian oscillation (MJO), and Rossby waves on surface wind and convection of the tropical Atlantic Ocean and African monsoon area. The results show that contribution from each process varies in different regions. In general, the MJO events dominate the westward-propagating Rossby waves in affecting strong convection in the African monsoon region. The Rossby waves, however, have larger contributions to convection in the western Atlantic Ocean. Both the westward- and eastward-propagating signals contribute approximately equally in the central Atlantic basin. The effects of intraseasonal signals have evident seasonality. Both convection amplitude and the number of strong convective events associated with the MJO are larger during November-April than during May-October in all regions. Convection associated with Rossby wave events is stronger during November-April for all regions, and the numbers of Rossby wave events are higher during November-April than during May-October in the African monsoon region, and are comparable for the two seasons in the western and central Atlantic basins. Of particular interest is that the MJOs originating from the Indo-Pacific Ocean can be enhanced over the tropical Atlantic Ocean while they propagate eastward, amplifying their impacts on the African monsoon. On the other hand, Rossby waves can originate either in the eastern equatorial Atlantic or West African monsoon region, and some can strengthen while they propagate westward, affecting surface winds and convection in the western Atlantic and Central American regions.Digital Object Identifier http://dx.doi.org/10.1175/JCLI-D-11-00528.1
收起
摘要 :
Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse...
展开
Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50-200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November-May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.
收起
摘要 :
The variation of relative humidity across West Africa during the dry season is evaluated using the Modern Era Retrospective Analysis for Research and Applications (MERRA) dataset and the method of self-organizing maps. Interest in...
展开
The variation of relative humidity across West Africa during the dry season is evaluated using the Modern Era Retrospective Analysis for Research and Applications (MERRA) dataset and the method of self-organizing maps. Interest in the dry season of West Africa is related to the connection between near-surface atmospheric moisture and the occurrence of meningitis across West Africa, most notably in the region known as the meningitis belt. The patterns in relative humidity are analyzed in terms of frequency of each pattern as well as the sequencing from one pattern to the next. The variations in relative humidity are characterized subannually for individual years from 1979 to 2009 as well as decadally over the entire 30-yr duration of dry seasons in West Africa. The progression from relatively moist patterns to relatively dry patterns and back to the moist patterns over the course of the dry season corresponds to the northward and then southward migration of the intertropical convergence zone. The results indicate distinctly different frequency and sequencing of relative humidity patterns from year to year. The year-to-year changes in relative humidity patterns are gradual. There is some indication of a larger, possibly decadal, pattern to the year-to-year changes in the variation of relative humidity over the course of the dry season. The results are reflective of the reanalysis data including potentially unusual and erroneously dry conditions in central Africa after the mid-1990s.
收起
摘要 :
The chmatology of the spatial structure functions of velocity and temperature for various altitudes (pressure levels) and latitude bands is constructed from the global rawinsonde network and from Aircraft Communications, Addressin...
展开
The chmatology of the spatial structure functions of velocity and temperature for various altitudes (pressure levels) and latitude bands is constructed from the global rawinsonde network and from Aircraft Communications, Addressing, and Reporting System/Aircraft Meteorological Data Relay (ACARS/AMDAR) data for the tropics and Northern Hemisphere. The ACARS/AMDAR data provide very dense coverage of winds and temperature over common commercial aircraft flight tracks and allow computation of structure functions to scales approaching 1 km, while the inclusion of rawinsonde data provides information on larger scales approaching 10 000 km. When taken together these data extend coverage of the spatial statistics of the atmosphere from previous studies to include larger geographic regions, lower altitudes, and a wider range of spatial scales. Simple empirical fits are used to approximate the structure function behavior as a function of altitude and latitude in the Northern Hemisphere. Results produced forspatial scales less than —2000 km are consistent with previous studies using other data sources. Estimates of the vertical and global horizontal structure of turbulence in terms of eddy dissipation rate e and thermal structure constant C~2_T are derivedfrom the structure function levels at the smaller scales.
收起
摘要 :
Accurate prediction of snowpack evolution and ablation is critical to supporting weather and hydrological applications. Convection-permitting modeling has been shown to well capture observed snowpack evolution over many western Un...
展开
Accurate prediction of snowpack evolution and ablation is critical to supporting weather and hydrological applications. Convection-permitting modeling has been shown to well capture observed snowpack evolution over many western United States (U.S.) mountain ranges, but some significant ablation biases still remain. In this study, we conduct process-level snowpack analyses of a widely used convection-permitting (4-km) weather research and forecasting (WRF) modeling product (WRF4km) for the contiguous U.S. to understand the mechanisms causing its unobserved early-spring snow ablation over Utah mountains. Analyses across Utah Snowpack Telemetry (SNOTEL) sites show that the unobserved snowpack ablation during mid-February to late-March in WRF4km is driven by multiple strong melting events. The melting results from the enhanced downward sensible heat flux to snowpack and enhanced ground solar radiation absorption, with generally larger contributions from the former before early March and from the latter after early March. The enhanced downward sensible heat flux to snowpack is mainly due to the enhanced surface heat exchange coefficient induced by high surface wind speeds. The enhanced ground solar radiation absorption is driven by both enhanced surface downward solar radiation and strong melting-induced snow cover reduction that is caused by deficiencies in Noah-MP snow-related parameterizations used in WRF4km. The substantial snow cover reduction during melting decreases surface albedo and hence triggers a positive albedo feedback that further accelerates melting. Our analyses reveal possible deficiencies in WRF and Noah-MP (e.g., canopy processes and snow albedo) and shed light on future directions for model improvements.
收起
摘要 :
The average signal spectrum (periodogram) for coherent Doppler lidar is calculated for a turbulent wind field. Simple approximations are compared with the exact calculation. The effects of random errors in the zero velocity refere...
展开
The average signal spectrum (periodogram) for coherent Doppler lidar is calculated for a turbulent wind field. Simple approximations are compared with the exact calculation. The effects of random errors in the zero velocity reference, the effects of averaging spectral estimates by use of multiple lidar pulses, and the effects of the range dependence of the lidar signal power over the range gate are included. For high spatial resolution measurements the lidar signal power is concentrated around one spectral (spectral bin), and correct interpretation of the contribution from turbulence is difficult because effects of spectral leakage. For range gates that are larger than the lidar pulse volume, the signal power is contained in many spectral bins and the effects of turbulence can be determined accurately for constant signal power over the range gate and for the far-field range dependence of the signal power.
收起
原文获取