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Abstract
The climatology. of cyclones and anticyclones is studied using a 2° latitude/longitude grid spanning North America and surrounding ocean environs for January and July 1950–77. In addition to determining total area statistics, areas distributions of cyclone/anticyclone events, genesis, decay and relative variability are analyzed, and preferred propagation tracks are determined. Further, the temporal variability of cyclone and anticyclone occurrences is examined.
In general, cyclones and anticyclones are more numerous, more intense, and displaced farther south in January than in July. Cyclogenesis occurs most frequently along the cast coast of the United States and in the lee of the Rocky Mountains, while anticyclones tend to originate in northern Canada and in the Great Plains region of the United States in January and in southwestern Canada in July. Cyclones generally propagate to the east and northeast from genetic regions, while anticyclones propagate to the east and southeast after forming. The Rocky Mountains are a barrier to cyclones penetrating eastward from the Pacific, while anticyclones tend to dissipate along the eastern coast of the United States. Although year-to-year fluctuations are quite prominent, the total number of both cyclones and anticyclones has decreased significantly over the 28-year period.
Abstract
The climatology. of cyclones and anticyclones is studied using a 2° latitude/longitude grid spanning North America and surrounding ocean environs for January and July 1950–77. In addition to determining total area statistics, areas distributions of cyclone/anticyclone events, genesis, decay and relative variability are analyzed, and preferred propagation tracks are determined. Further, the temporal variability of cyclone and anticyclone occurrences is examined.
In general, cyclones and anticyclones are more numerous, more intense, and displaced farther south in January than in July. Cyclogenesis occurs most frequently along the cast coast of the United States and in the lee of the Rocky Mountains, while anticyclones tend to originate in northern Canada and in the Great Plains region of the United States in January and in southwestern Canada in July. Cyclones generally propagate to the east and northeast from genetic regions, while anticyclones propagate to the east and southeast after forming. The Rocky Mountains are a barrier to cyclones penetrating eastward from the Pacific, while anticyclones tend to dissipate along the eastern coast of the United States. Although year-to-year fluctuations are quite prominent, the total number of both cyclones and anticyclones has decreased significantly over the 28-year period.
Abstract
The continuous release of very small, but detectable amounts of 131I from the Brookhaven Graphite Research Reactor has provided an unusual opportunity for verification of dispersion estimates based on meteorological parameters. Analysis of an entire year's data shows the prediction system to be reliable within a factor of 2, and contains valuable information about such factors as effective stack height, the effectiveness of simplified dispersion models, and the variation of dispersion parameters with season and trajectory.
Abstract
The continuous release of very small, but detectable amounts of 131I from the Brookhaven Graphite Research Reactor has provided an unusual opportunity for verification of dispersion estimates based on meteorological parameters. Analysis of an entire year's data shows the prediction system to be reliable within a factor of 2, and contains valuable information about such factors as effective stack height, the effectiveness of simplified dispersion models, and the variation of dispersion parameters with season and trajectory.
Abstract
Extended precipitation forecasts, with leads of weeks to seasons, are valuable for planning water use and are produced by the U.S. National Weather Service. Forecast skill tends to be low and any skill improvement could be valuable. Here, methods are discussed for improving statistical precipitation forecasting over the contiguous United States. Monthly precipitation is forecast using predictors from the previous month. Testing shows that improvements are obtained from both improved statistical methods and from the use of satellite-based ocean-area precipitation predictors. The statistical superensemble method gives higher skill compared to traditional statistical forecasting. Ensemble statistical forecasting combines individual forecasts. The proposed superensemble is a weighted mean of many forecasts or of forecasts from different prediction systems and uses the forecast reliability estimate to define weights. The method is tested with different predictors to show its skill and how skill can be improved using additional predictors. Cross validation is used to evaluate the skill. Although predictions are strongly influenced by ENSO, in the superensemble other regions contribute more to the forecast skill. The superensemble optimally combines forecasts based on different predictor regions and predictor types. The contribution from multiple predictor regions improves skill and reduces the ENSO spring barrier. Adding satellite-based ocean-area precipitation predictors noticeably increases forecast skill. The resulting skill is comparable to that from dynamic-model forecasts, but the regions with best forecast skill may be different. This paper shows that the statistical superensemble forecasts may be complementary to dynamic forecasts and that combining them may further increase forecast skill.
Abstract
Extended precipitation forecasts, with leads of weeks to seasons, are valuable for planning water use and are produced by the U.S. National Weather Service. Forecast skill tends to be low and any skill improvement could be valuable. Here, methods are discussed for improving statistical precipitation forecasting over the contiguous United States. Monthly precipitation is forecast using predictors from the previous month. Testing shows that improvements are obtained from both improved statistical methods and from the use of satellite-based ocean-area precipitation predictors. The statistical superensemble method gives higher skill compared to traditional statistical forecasting. Ensemble statistical forecasting combines individual forecasts. The proposed superensemble is a weighted mean of many forecasts or of forecasts from different prediction systems and uses the forecast reliability estimate to define weights. The method is tested with different predictors to show its skill and how skill can be improved using additional predictors. Cross validation is used to evaluate the skill. Although predictions are strongly influenced by ENSO, in the superensemble other regions contribute more to the forecast skill. The superensemble optimally combines forecasts based on different predictor regions and predictor types. The contribution from multiple predictor regions improves skill and reduces the ENSO spring barrier. Adding satellite-based ocean-area precipitation predictors noticeably increases forecast skill. The resulting skill is comparable to that from dynamic-model forecasts, but the regions with best forecast skill may be different. This paper shows that the statistical superensemble forecasts may be complementary to dynamic forecasts and that combining them may further increase forecast skill.
Abstract
As a preliminary step in evaluating the feasibility of determining meaningful tropospheric humidity trends on a hemispheric or global scale using a sparse radiosonde network, radiosonde data at the earth's surface and at 850, 700 and 500 mb mandatory pressure surfaces, and significant levels between, have been examined for the interval 1958-80 at Brownsville, Texas and Great Falls, Montana. Adjustments had to be applied to the data prior to 1966 because at this earlier time dry observations (“motorboating”) were not reported. In general, the relative humidity at these two stations decreased or remained constant between 1958 and about 1970, and increased between about 1970 and 1980, but over the full record, it decreased at Brownsville and increased at Great Falls. Mixing ratio and precipitable water decreased during the earlier interval and increased during the later interval, similar to the variation in Northern Hemisphere temperature, although this may well be coincidence. On the seasonal and yearly time scale the relative humidity has tended to vary inversely with station temperature, and mixing ratio directly with this temperature, but these two stations do not define the relation among long-term trends in temperature, relative humidity and mixing ratio. It is concluded that to establish hemispheric or global trends in humidity will require use of a fairly extensive radiosonde network, as well as knowledge of instrumental changes and changes in measurement technique at individual stations within this network.
Abstract
As a preliminary step in evaluating the feasibility of determining meaningful tropospheric humidity trends on a hemispheric or global scale using a sparse radiosonde network, radiosonde data at the earth's surface and at 850, 700 and 500 mb mandatory pressure surfaces, and significant levels between, have been examined for the interval 1958-80 at Brownsville, Texas and Great Falls, Montana. Adjustments had to be applied to the data prior to 1966 because at this earlier time dry observations (“motorboating”) were not reported. In general, the relative humidity at these two stations decreased or remained constant between 1958 and about 1970, and increased between about 1970 and 1980, but over the full record, it decreased at Brownsville and increased at Great Falls. Mixing ratio and precipitable water decreased during the earlier interval and increased during the later interval, similar to the variation in Northern Hemisphere temperature, although this may well be coincidence. On the seasonal and yearly time scale the relative humidity has tended to vary inversely with station temperature, and mixing ratio directly with this temperature, but these two stations do not define the relation among long-term trends in temperature, relative humidity and mixing ratio. It is concluded that to establish hemispheric or global trends in humidity will require use of a fairly extensive radiosonde network, as well as knowledge of instrumental changes and changes in measurement technique at individual stations within this network.
Abstract
The sampling error variances of the 5° × 5° Global Historical Climatological Network (GHCN) monthly surface air temperature data are estimated from January 1851 to December 2001. For each GHCN grid box and for each month in the above time interval, an error variance is computed. The authors’ error estimation is determined by two parameters: the spatial variance and a correlation factor determined by using a regression. The error estimation procedures have the following steps. First, for a given month for each grid box with at least four station anomalies, the spatial variance of the grid box’s temperature anomaly, σ̂ 2 s , is calculated by using a 5-yr moving time window (MTW). Second, for each grid box with at least four stations, a regression is applied to find a correlation factor, αs , in the same 5-yr MTW. Third, spatial interpolation is used to fill the spatial variance and the correlation factor in grid boxes with less than four stations. Fourth, the sampling error variance is calculated by using the formula E 2 = αsσ̂ 2 s /N, where N is the total number of observations for the grid box in the given month. The two parameters of the authors’ error estimation are compared with those of the University of East Anglia’s Climatic Research Unit for the decadal data. The comparison shows a close agreement of the parameters’ values for decadal data. An advantage of this new method is the generation of monthly error estimates. The authors’ error product will be available at the U.S. National Climatic Data Center.
Abstract
The sampling error variances of the 5° × 5° Global Historical Climatological Network (GHCN) monthly surface air temperature data are estimated from January 1851 to December 2001. For each GHCN grid box and for each month in the above time interval, an error variance is computed. The authors’ error estimation is determined by two parameters: the spatial variance and a correlation factor determined by using a regression. The error estimation procedures have the following steps. First, for a given month for each grid box with at least four station anomalies, the spatial variance of the grid box’s temperature anomaly, σ̂ 2 s , is calculated by using a 5-yr moving time window (MTW). Second, for each grid box with at least four stations, a regression is applied to find a correlation factor, αs , in the same 5-yr MTW. Third, spatial interpolation is used to fill the spatial variance and the correlation factor in grid boxes with less than four stations. Fourth, the sampling error variance is calculated by using the formula E 2 = αsσ̂ 2 s /N, where N is the total number of observations for the grid box in the given month. The two parameters of the authors’ error estimation are compared with those of the University of East Anglia’s Climatic Research Unit for the decadal data. The comparison shows a close agreement of the parameters’ values for decadal data. An advantage of this new method is the generation of monthly error estimates. The authors’ error product will be available at the U.S. National Climatic Data Center.
Abstract
The longest record of precipitation estimated from satellites is the outgoing longwave radiation (OLR) precipitation index (OPI), which is based on polar-orbiting infrared observations from the Advanced Very High Resolution Radiometer (AVHRR) instrument that has flown onboard successive NOAA satellites. A significant barrier to the use of these data in studies of the climate of tropical precipitation (among other things) is the large bias caused by orbital drift that is present in the OLR data. Because the AVHRR instruments are deployed on the polar-orbiting spacecraft, OLR observations are recorded at specific times for each earth location for each day. Discontinuities are caused by the use of multiple satellites with different observing times as well as the orbital drift that occurs throughout the lifetime of each satellite. A regression-based correction is proposed based solely on the equator crossing time (ECT). The correction allows for separate means for each satellite as well as separate coefficients for each satellite ECT. The correction is calculated separately for each grid box but is applied only at locations where the correction is correlated with the OLR estimate. Thus, the correction is applied only where deemed necessary.
The OPI is used to estimate precipitation from the OLR estimates based on the new corrected version of the OLR, the uncorrected OLR, and two earlier published corrected versions. One of the earlier corrections is derived by removing variations from AVHRR based on EOFs that are identified as containing spurious variations related to the ECT bias, whereas the other is based on OLR estimates from the High Resolution Infrared Radiation Sounder (HIRS) that have been corrected using diurnal models for each grid box. The new corrected version is shown to be free of nearly all of the ECT bias and has the lowest root mean square difference when compared to gauges and passive microwave estimates of precipitation. The EOF-based correction fails to remove all of the variations related to the ECT bias, whereas the correction based on HIRS removes much of the bias but appears to introduce erroneous trends caused by the water vapor signal to which these data are sensitive. The new correction for AVHRR OLR works well in the tropics where the OPI has the most skill, but users should be careful when interpreting trends outside this region.
Abstract
The longest record of precipitation estimated from satellites is the outgoing longwave radiation (OLR) precipitation index (OPI), which is based on polar-orbiting infrared observations from the Advanced Very High Resolution Radiometer (AVHRR) instrument that has flown onboard successive NOAA satellites. A significant barrier to the use of these data in studies of the climate of tropical precipitation (among other things) is the large bias caused by orbital drift that is present in the OLR data. Because the AVHRR instruments are deployed on the polar-orbiting spacecraft, OLR observations are recorded at specific times for each earth location for each day. Discontinuities are caused by the use of multiple satellites with different observing times as well as the orbital drift that occurs throughout the lifetime of each satellite. A regression-based correction is proposed based solely on the equator crossing time (ECT). The correction allows for separate means for each satellite as well as separate coefficients for each satellite ECT. The correction is calculated separately for each grid box but is applied only at locations where the correction is correlated with the OLR estimate. Thus, the correction is applied only where deemed necessary.
The OPI is used to estimate precipitation from the OLR estimates based on the new corrected version of the OLR, the uncorrected OLR, and two earlier published corrected versions. One of the earlier corrections is derived by removing variations from AVHRR based on EOFs that are identified as containing spurious variations related to the ECT bias, whereas the other is based on OLR estimates from the High Resolution Infrared Radiation Sounder (HIRS) that have been corrected using diurnal models for each grid box. The new corrected version is shown to be free of nearly all of the ECT bias and has the lowest root mean square difference when compared to gauges and passive microwave estimates of precipitation. The EOF-based correction fails to remove all of the variations related to the ECT bias, whereas the correction based on HIRS removes much of the bias but appears to introduce erroneous trends caused by the water vapor signal to which these data are sensitive. The new correction for AVHRR OLR works well in the tropics where the OPI has the most skill, but users should be careful when interpreting trends outside this region.
Abstract
A new boundary layer turbulent mixing scheme has been developed for use in the UKMO weather forecasting and climate prediction models. This includes a representation of nonlocal mixing (driven by both surface fluxes and cloud-top processes) in unstable layers, either coupled to or decoupled from the surface, and an explicit entrainment parameterization. The scheme is formulated in moist conserved variables so that it can treat both dry and cloudy layers. Details of the scheme and examples of its performance in single-column model tests are presented.
Abstract
A new boundary layer turbulent mixing scheme has been developed for use in the UKMO weather forecasting and climate prediction models. This includes a representation of nonlocal mixing (driven by both surface fluxes and cloud-top processes) in unstable layers, either coupled to or decoupled from the surface, and an explicit entrainment parameterization. The scheme is formulated in moist conserved variables so that it can treat both dry and cloudy layers. Details of the scheme and examples of its performance in single-column model tests are presented.
Abstract
A new turbulent mixing scheme, described in Part I of this paper, is tested in the climate and mesoscale configurations of the U.K. Met. Office’s Unified Model (UM). In climate configuration, the scheme is implemented along with increased vertical resolution below 700 hPa (the same as that in the mesoscale model), in order to allow the different boundary layer types and processes to be identified and treated properly. In both configurations, the new boundary layer (PBL-N) mixing scheme produces some improvement over the current boundary layer (PBL-C) scheme. The PBL-N scheme is able to diagnose different boundary layer types that appear to be consistent with the observed conditions, and the boundary layer structure is improved in comparison with observations. In the climate model, the boundary layer and cloud structure in the semipermanent stratocumulus regions of the eastern subtropical oceans are noticeably improved with the PBL-N scheme. The deepening and decoupling of the boundary layer toward the trade cumulus regime is also simulated more realistically. However, the cloud amounts in the stratocumulus regions, which were underestimated with the PBL-C scheme, are reduced further when the PBL-N scheme is included. Tests of the PBL-N scheme in the UM single-column model and in a development version of the UM, where the dynamics, time stepping, and vertical grid are different from the standard version, both show that realistic stratocumulus cloud amounts can be achieved. Thus, it is thought that the performance of the PBL-N scheme in the standard UM may be being limited by other aspects of that model. In the mesoscale model, improvements in the simulation of a convective case are achieved with the PBL-N scheme through reductions in layer cloud amount, while the simulation of a stratocumulus case is improved through better representation of the cloud and boundary layer structure. Other mesoscale model case studies show that there is a consistent improvement in fog probabilities and forecasts of cloud-base height. The root-mean-square errors in screen-level temperature are also reduced slightly. The weak daytime bias in wind strength is improved greatly through a systematic increase in the 10-m wind speed in unstable conditions. As a result of these trials, the scheme has been implemented operationally in the mesoscale model.
Abstract
A new turbulent mixing scheme, described in Part I of this paper, is tested in the climate and mesoscale configurations of the U.K. Met. Office’s Unified Model (UM). In climate configuration, the scheme is implemented along with increased vertical resolution below 700 hPa (the same as that in the mesoscale model), in order to allow the different boundary layer types and processes to be identified and treated properly. In both configurations, the new boundary layer (PBL-N) mixing scheme produces some improvement over the current boundary layer (PBL-C) scheme. The PBL-N scheme is able to diagnose different boundary layer types that appear to be consistent with the observed conditions, and the boundary layer structure is improved in comparison with observations. In the climate model, the boundary layer and cloud structure in the semipermanent stratocumulus regions of the eastern subtropical oceans are noticeably improved with the PBL-N scheme. The deepening and decoupling of the boundary layer toward the trade cumulus regime is also simulated more realistically. However, the cloud amounts in the stratocumulus regions, which were underestimated with the PBL-C scheme, are reduced further when the PBL-N scheme is included. Tests of the PBL-N scheme in the UM single-column model and in a development version of the UM, where the dynamics, time stepping, and vertical grid are different from the standard version, both show that realistic stratocumulus cloud amounts can be achieved. Thus, it is thought that the performance of the PBL-N scheme in the standard UM may be being limited by other aspects of that model. In the mesoscale model, improvements in the simulation of a convective case are achieved with the PBL-N scheme through reductions in layer cloud amount, while the simulation of a stratocumulus case is improved through better representation of the cloud and boundary layer structure. Other mesoscale model case studies show that there is a consistent improvement in fog probabilities and forecasts of cloud-base height. The root-mean-square errors in screen-level temperature are also reduced slightly. The weak daytime bias in wind strength is improved greatly through a systematic increase in the 10-m wind speed in unstable conditions. As a result of these trials, the scheme has been implemented operationally in the mesoscale model.
