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Abstract
The parameterization of evaporation of rain and snow in large-scale numerical models of the atmosphere is considered. Evaporation coefficients dependent on the precipitation rate are derived following the method of Kessler for both stratiform and convective precipitation and compared with the calculations of more detailed models and observations using passive models. The derived “bulk” parameterizations are in good agreement with the evaporation rates derived from the microphysical model of Clough and Franks, showing more rapid evaporation of snow than rain. Comparison is made to other recent evaporation parameterizations and the sensitivity of the estimated evaporation rate to the nature of precipitation, and the motion of the air through which it falls is also studied. The impact of the inclusion of different rates for the evaporation of stratiform rain and snow upon climate simulations by the Meteorological Office Unified Model (the Hadley Centre Climate Model) is considered.
Abstract
The parameterization of evaporation of rain and snow in large-scale numerical models of the atmosphere is considered. Evaporation coefficients dependent on the precipitation rate are derived following the method of Kessler for both stratiform and convective precipitation and compared with the calculations of more detailed models and observations using passive models. The derived “bulk” parameterizations are in good agreement with the evaporation rates derived from the microphysical model of Clough and Franks, showing more rapid evaporation of snow than rain. Comparison is made to other recent evaporation parameterizations and the sensitivity of the estimated evaporation rate to the nature of precipitation, and the motion of the air through which it falls is also studied. The impact of the inclusion of different rates for the evaporation of stratiform rain and snow upon climate simulations by the Meteorological Office Unified Model (the Hadley Centre Climate Model) is considered.
Abstract
Snowpack, as measured on 1 April, is the primary source of warm-season streamflow for most of the western United States and thus represents an important source of water supply. An understanding of climate factors that influence the variability of this water supply and thus its predictability is important for water resource management. In this study, principal component analysis is used to identify the primary modes of 1 April snowpack variability in the western United States. Two components account for 61% of the total snowpack variability in the western United States. Relations between these modes of variability and indices of Pacific Ocean climate [e.g., the Pacific decadal oscillation (PDO) and Niño-3 sea surface temperatures (SSTs)] are examined. The first mode of snowpack variability is closely associated with the PDO, whereas the second mode varies in concert with both the PDO and Niño-3 SSTs. Because these atmospheric–oceanic conditions change slowly from season to season, the observed teleconnections between the Pacific Ocean climate and 1 April snowpack may be useful to forecast 1 April snowpack using data that describe the Pacific Ocean climate in the previous summer and autumn seasons, especially for the northwestern United States.
Abstract
Snowpack, as measured on 1 April, is the primary source of warm-season streamflow for most of the western United States and thus represents an important source of water supply. An understanding of climate factors that influence the variability of this water supply and thus its predictability is important for water resource management. In this study, principal component analysis is used to identify the primary modes of 1 April snowpack variability in the western United States. Two components account for 61% of the total snowpack variability in the western United States. Relations between these modes of variability and indices of Pacific Ocean climate [e.g., the Pacific decadal oscillation (PDO) and Niño-3 sea surface temperatures (SSTs)] are examined. The first mode of snowpack variability is closely associated with the PDO, whereas the second mode varies in concert with both the PDO and Niño-3 SSTs. Because these atmospheric–oceanic conditions change slowly from season to season, the observed teleconnections between the Pacific Ocean climate and 1 April snowpack may be useful to forecast 1 April snowpack using data that describe the Pacific Ocean climate in the previous summer and autumn seasons, especially for the northwestern United States.
Abstract
An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.
Abstract
An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.
Abstract
The 2-yr performance of a pseudo-operational (real time) limited-area ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting Model is described. This system assimilates conventional observations from surface stations, rawinsondes, the Aircraft Communications Addressing and Reporting System (ACARS), and cloud motion vectors every 6 h on a domain that includes the eastern North Pacific Ocean and western North America. Ensemble forecasts from this system and deterministic output from operational numerical weather prediction models during this same period are verified against rawinsonde and surface observation data. Relative to operational forecasts, the forecast from the ensemble-mean analysis has slightly larger errors in wind and temperature but smaller errors in moisture, even though satellite radiances are not assimilated by the EnKF. Time-averaged correlations indicate that assimilating ACARS and cloud wind data with flow-dependent error statistics provides corrections to the moisture field in the absence of direct observations of that field. Comparison with a control experiment in which a deterministic forecast is cycled without observation assimilation indicates that the skill in the EnKF’s forecasts results from assimilating observations and not from lateral boundary conditions or the model formulation. Furthermore, the ensemble variance is generally in good agreement with the ensemble-mean error and the spread increases monotonically with forecast hour.
Abstract
The 2-yr performance of a pseudo-operational (real time) limited-area ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting Model is described. This system assimilates conventional observations from surface stations, rawinsondes, the Aircraft Communications Addressing and Reporting System (ACARS), and cloud motion vectors every 6 h on a domain that includes the eastern North Pacific Ocean and western North America. Ensemble forecasts from this system and deterministic output from operational numerical weather prediction models during this same period are verified against rawinsonde and surface observation data. Relative to operational forecasts, the forecast from the ensemble-mean analysis has slightly larger errors in wind and temperature but smaller errors in moisture, even though satellite radiances are not assimilated by the EnKF. Time-averaged correlations indicate that assimilating ACARS and cloud wind data with flow-dependent error statistics provides corrections to the moisture field in the absence of direct observations of that field. Comparison with a control experiment in which a deterministic forecast is cycled without observation assimilation indicates that the skill in the EnKF’s forecasts results from assimilating observations and not from lateral boundary conditions or the model formulation. Furthermore, the ensemble variance is generally in good agreement with the ensemble-mean error and the spread increases monotonically with forecast hour.
Abstract
The sensitivity of forecasts to observations is evaluated using an ensemble approach with data drawn from a pseudo-operational ensemble Kalman filter. For Gaussian statistics and a forecast metric defined as a scalar function of the forecast variables, the effect of observations on the forecast metric is quantified by changes in the metric mean and variance. For a single observation, expressions for these changes involve a product of scalar quantities, which can be rapidly evaluated for large numbers of observations. This technique is applied to determining climatological forecast sensitivity and predicting the impact of observations on sea level pressure and precipitation forecast metrics. The climatological 24-h forecast sensitivity of the average pressure over western Washington State shows a region of maximum sensitivity to the west of the region, which tilts gently westward with height. The accuracy of ensemble sensitivity predictions is tested by withholding a single buoy pressure observation from this region and comparing this perturbed forecast with the control case where the buoy is assimilated. For 30 cases, there is excellent agreement between these forecast differences and the ensemble predictions, as measured by the forecast metric. This agreement decreases for increasing numbers of observations. Nevertheless, by using statistical confidence tests to address sampling error, the impact of thousands of observations on forecast-metric variance is shown to be well estimated by a subset of the O(100) most significant observations.
Abstract
The sensitivity of forecasts to observations is evaluated using an ensemble approach with data drawn from a pseudo-operational ensemble Kalman filter. For Gaussian statistics and a forecast metric defined as a scalar function of the forecast variables, the effect of observations on the forecast metric is quantified by changes in the metric mean and variance. For a single observation, expressions for these changes involve a product of scalar quantities, which can be rapidly evaluated for large numbers of observations. This technique is applied to determining climatological forecast sensitivity and predicting the impact of observations on sea level pressure and precipitation forecast metrics. The climatological 24-h forecast sensitivity of the average pressure over western Washington State shows a region of maximum sensitivity to the west of the region, which tilts gently westward with height. The accuracy of ensemble sensitivity predictions is tested by withholding a single buoy pressure observation from this region and comparing this perturbed forecast with the control case where the buoy is assimilated. For 30 cases, there is excellent agreement between these forecast differences and the ensemble predictions, as measured by the forecast metric. This agreement decreases for increasing numbers of observations. Nevertheless, by using statistical confidence tests to address sampling error, the impact of thousands of observations on forecast-metric variance is shown to be well estimated by a subset of the O(100) most significant observations.
Abstract
Passive microwave brightness temperatures from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) can be used to infer the soil moisture content over agricultural areas such as the southern Great Plains of the United States. A linear regression analysis between three transforms of the five dual polarized SMMR wavelengths of 0.81, 1.36, 1.66, 2.80 and 4.54 cm and an antecedent precipitation index representing the precipitation history showed correlation coefficients greater than 0.90 for pixel aggregates of 25–50 km. The use of surface air temperatures to approximate the temperature of the emitting layer was not required to obtain high correlation coefficients between the transforms and the antecedent precipitation index.
Abstract
Passive microwave brightness temperatures from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) can be used to infer the soil moisture content over agricultural areas such as the southern Great Plains of the United States. A linear regression analysis between three transforms of the five dual polarized SMMR wavelengths of 0.81, 1.36, 1.66, 2.80 and 4.54 cm and an antecedent precipitation index representing the precipitation history showed correlation coefficients greater than 0.90 for pixel aggregates of 25–50 km. The use of surface air temperatures to approximate the temperature of the emitting layer was not required to obtain high correlation coefficients between the transforms and the antecedent precipitation index.
Abstract
Comparison between in situ aircraft observations of temperature and National Meteorological Center and Global Weather Central analysis fields of temperature is presented for a continental and oceanic flight route. The standard deviations of the temperature differences over several hundred flights are found to be 2.5 and 3.5°C for the continental and oceanic route, respectively. A bias towards warm temperatures of about 0.85°C for the analysis fields was found for the oceanic route. Only small differences are found between the NMC and GWC analysis field temperatures.
Abstract
Comparison between in situ aircraft observations of temperature and National Meteorological Center and Global Weather Central analysis fields of temperature is presented for a continental and oceanic flight route. The standard deviations of the temperature differences over several hundred flights are found to be 2.5 and 3.5°C for the continental and oceanic route, respectively. A bias towards warm temperatures of about 0.85°C for the analysis fields was found for the oceanic route. Only small differences are found between the NMC and GWC analysis field temperatures.
Abstract
In light of the upcoming launch of the Global Precipitation Measurement (GPM) mission, a parametric retrieval algorithm of the nonraining parameters over the global oceans is developed with the ability to accommodate all currently existing and planned spaceborne microwave window channel sensors and imagers. The physical retrieval is developed using all available sensor channels in a full optimal estimation inversion. This framework requires that retrieved parameters be physically consistent with all observed satellite radiances regardless of the sensor being used. The retrieval algorithm has been successfully applied to the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the Special Sensor Microwave Imager (SSM/I), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) with geophysical parameter retrieval results comparable to independent studies using sensor-optimized algorithms. The optimal estimation diagnostics characterize the retrieval further, providing errors associated with each of the retrieved parameters, indicating whether the retrieved state is physically consistent with observed radiances, and yielding information on how well simulated radiances agree with observed radiances. This allows for the quantitative assessment of potential calibration issues in either the model or sensor. In addition, there is an expected, consistent response of these diagnostics based on the scene being observed, such as in the case of a raining scene, allowing for the emergence of a rainfall detection scheme providing a new capability in rainfall identification for use in passive microwave rainfall and cloud property retrievals.
Abstract
In light of the upcoming launch of the Global Precipitation Measurement (GPM) mission, a parametric retrieval algorithm of the nonraining parameters over the global oceans is developed with the ability to accommodate all currently existing and planned spaceborne microwave window channel sensors and imagers. The physical retrieval is developed using all available sensor channels in a full optimal estimation inversion. This framework requires that retrieved parameters be physically consistent with all observed satellite radiances regardless of the sensor being used. The retrieval algorithm has been successfully applied to the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the Special Sensor Microwave Imager (SSM/I), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) with geophysical parameter retrieval results comparable to independent studies using sensor-optimized algorithms. The optimal estimation diagnostics characterize the retrieval further, providing errors associated with each of the retrieved parameters, indicating whether the retrieved state is physically consistent with observed radiances, and yielding information on how well simulated radiances agree with observed radiances. This allows for the quantitative assessment of potential calibration issues in either the model or sensor. In addition, there is an expected, consistent response of these diagnostics based on the scene being observed, such as in the case of a raining scene, allowing for the emergence of a rainfall detection scheme providing a new capability in rainfall identification for use in passive microwave rainfall and cloud property retrievals.
Abstract
Aircraft measurements of winds and temperatures collected during the GASP program are used to study the effects of topography as a source of mesoscale variability. Variances of fluctuations at the mesoscale over rough terrain are enhanced up to nearly two orders of magnitude compared to nonsource regions in some cases and are frequently enhanced by an order of magnitude. The implications of these episodic enhancements of variances for the vertical transports of energy and momentum are considered in the framework of gravity wave theory. The observed flight data are used to estimate the momentum flux
Abstract
Aircraft measurements of winds and temperatures collected during the GASP program are used to study the effects of topography as a source of mesoscale variability. Variances of fluctuations at the mesoscale over rough terrain are enhanced up to nearly two orders of magnitude compared to nonsource regions in some cases and are frequently enhanced by an order of magnitude. The implications of these episodic enhancements of variances for the vertical transports of energy and momentum are considered in the framework of gravity wave theory. The observed flight data are used to estimate the momentum flux
Abstract
We present studies of four cases of mesoscale variance enhancements of horizontal velocity and temperature due to frontal activity, nonfrontal convection, and wind shear. These data were obtained aboard commercial aircraft during the Global Atmospheric Sampling Program (GASP) in 1978 and 1979 and from the corresponding meteorological analyses and satellite imagery. Additional GASP data were used to permit a statistical assessment of the importance of various sources of enhanced variances. Our results, and those in a companion paper addressing the variance enhancements associated with topography, represent refinements of previous source analyses using the GASP dataset. Significant findings include mean variance enhancements of velocity and temperature due to convection and jet-stream flow ranging from ∼2 to 8 for 64-km and 256-km data segments, and enhancements for individual segments as high as ∼20 to 100. The mean 64-km variance enhancement for all variables and source types, relative to a quiescent background, was estimated to be 6.1. These results suggest a major role for localized sources in energizing the mesoscale motion spectrum at horizontal scales < ∼100 km, and correspondingly greater influences for such motions at greater heights.
Abstract
We present studies of four cases of mesoscale variance enhancements of horizontal velocity and temperature due to frontal activity, nonfrontal convection, and wind shear. These data were obtained aboard commercial aircraft during the Global Atmospheric Sampling Program (GASP) in 1978 and 1979 and from the corresponding meteorological analyses and satellite imagery. Additional GASP data were used to permit a statistical assessment of the importance of various sources of enhanced variances. Our results, and those in a companion paper addressing the variance enhancements associated with topography, represent refinements of previous source analyses using the GASP dataset. Significant findings include mean variance enhancements of velocity and temperature due to convection and jet-stream flow ranging from ∼2 to 8 for 64-km and 256-km data segments, and enhancements for individual segments as high as ∼20 to 100. The mean 64-km variance enhancement for all variables and source types, relative to a quiescent background, was estimated to be 6.1. These results suggest a major role for localized sources in energizing the mesoscale motion spectrum at horizontal scales < ∼100 km, and correspondingly greater influences for such motions at greater heights.
