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- Author or Editor: A. Hollingsworth x
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We examine the tropical wind field analyses produced by a recent assimilation of the Final FGGE II-b dataset. Our aim is to estimate the effects, on the tropical wind analyses, of biases in the data and biases in the assimilation system. The assimilation system was similar to that used operationally at ECMWF in the first half of 1985. The period studied is the first Special Observing Period (SOP-1).
Important differences occur in the intensity of divergence at upper and lower levels in the western Pacific, as measured by cloud-track winds (SATOBs) and by rawinsondes (TEMPs). There appear to be important biases also in the SATOB estimates of the zonal flow at upper and lower levels in the eastern Pacific. There are substantial biases in the wind directions at some west African stations.
The 6-hour forecasts which provide the background fields for the analyses show important underestimates of the mean intensity of the tropical divergence field, particularly in the equatorial western Pacific. The errors in the background field probably occur because of underestimation of the intensity of tropical convection in the diabatic initialization and in the course of the forecast; the heavy spatial smoothing applied to the convective heating in the initialization probably also plays a role.
Data were available in sufficient quantities that the analysis algorithm corrected the mean errors in the background field to a very large extent. As a result, any residual uncertainty in the mean analyses is within the uncertainty of the observations. The analysis algorithm has a rather poor response to divergent information even on large scales, so the analyzed divergence field agrees best with the observational data showing the weakest divergence, both in the upper and lower troposphere. The mean analyzed divergence field in the west Pacific agrees with the 850 mb TEMP data but is weaker than the intensity suggested by the low-level SATOBs and the SHIPs. In the upper troposphere the analyzed divergence is weaker than that suggested by the TEMPS, but agrees with that suggested by the (probably less reliable) SATOBs. Thus in this important area in the tropics the biases in the new analyses of the mean divergent wind field appear to be within the range of biases in the data, but the divergence is probably still underestimated in the upper troposphere and near the surface.
Abstract
We examine the tropical wind field analyses produced by a recent assimilation of the Final FGGE II-b dataset. Our aim is to estimate the effects, on the tropical wind analyses, of biases in the data and biases in the assimilation system. The assimilation system was similar to that used operationally at ECMWF in the first half of 1985. The period studied is the first Special Observing Period (SOP-1).
Important differences occur in the intensity of divergence at upper and lower levels in the western Pacific, as measured by cloud-track winds (SATOBs) and by rawinsondes (TEMPs). There appear to be important biases also in the SATOB estimates of the zonal flow at upper and lower levels in the eastern Pacific. There are substantial biases in the wind directions at some west African stations.
The 6-hour forecasts which provide the background fields for the analyses show important underestimates of the mean intensity of the tropical divergence field, particularly in the equatorial western Pacific. The errors in the background field probably occur because of underestimation of the intensity of tropical convection in the diabatic initialization and in the course of the forecast; the heavy spatial smoothing applied to the convective heating in the initialization probably also plays a role.
Data were available in sufficient quantities that the analysis algorithm corrected the mean errors in the background field to a very large extent. As a result, any residual uncertainty in the mean analyses is within the uncertainty of the observations. The analysis algorithm has a rather poor response to divergent information even on large scales, so the analyzed divergence field agrees best with the observational data showing the weakest divergence, both in the upper and lower troposphere. The mean analyzed divergence field in the west Pacific agrees with the 850 mb TEMP data but is weaker than the intensity suggested by the low-level SATOBs and the SHIPs. In the upper troposphere the analyzed divergence is weaker than that suggested by the TEMPS, but agrees with that suggested by the (probably less reliable) SATOBs. Thus in this important area in the tropics the biases in the new analyses of the mean divergent wind field appear to be within the range of biases in the data, but the divergence is probably still underestimated in the upper troposphere and near the surface.
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The forecast skill of the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (EPS) in predicting precipitation probabilities is discussed. Four seasons are analyzed in detail using signal detection theory and reliability diagrams to define objective measure of predictive skill.
First, the EPS performance during summer 1997 is discussed. Attention is focused on Europe and two European local regions, one centered around the Alps and the other around Ireland. Results indicate that for Europe the EPS can give skillful prediction of low precipitation amounts [i.e., lower than 2 mm (12 h)−1] up to forecast day 6, and of high precipitation amounts [i.e., between 2 and 10 mm (12 h)−1] up to day 4. Lower levels of skill are achieved for smaller local areas.
Then, the EPS performance during summer 1996 (i.e., prior to the enhancement introduced on 10 December 1996 from 33 to 51 members and to resolution increase from T63 L19 to TL159 L31) and summer 1997 are compared. Results show that the EPS has been remarkably more skillful during summer 1997 than summer 1996, with the gain in predictability up to 3 days for the highest [5 and 10 mm (12 h)−1] amounts of precipitation.
Finally, the EPS performance during wintertime is analyzed. Two issues are investigated: the seasonal variability of the forecast skill of the new EPS, and the impact of the system upgrade on the wintertime performance. The comparison of the performance of the new EPS system during winter 1996/97 and during summer 1997 indicates that the EPS is more skillful during winter than during summer, with differences in predictive skill around 3 days for precipitation amounts larger than 2 mm (12 h)−1. The comparison of the EPS performance before and after the system upgrade on 10 December 1996 during winter confirms the summer conclusion that the upgraded system is more skillful than the old one.
Abstract
The forecast skill of the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (EPS) in predicting precipitation probabilities is discussed. Four seasons are analyzed in detail using signal detection theory and reliability diagrams to define objective measure of predictive skill.
First, the EPS performance during summer 1997 is discussed. Attention is focused on Europe and two European local regions, one centered around the Alps and the other around Ireland. Results indicate that for Europe the EPS can give skillful prediction of low precipitation amounts [i.e., lower than 2 mm (12 h)−1] up to forecast day 6, and of high precipitation amounts [i.e., between 2 and 10 mm (12 h)−1] up to day 4. Lower levels of skill are achieved for smaller local areas.
Then, the EPS performance during summer 1996 (i.e., prior to the enhancement introduced on 10 December 1996 from 33 to 51 members and to resolution increase from T63 L19 to TL159 L31) and summer 1997 are compared. Results show that the EPS has been remarkably more skillful during summer 1997 than summer 1996, with the gain in predictability up to 3 days for the highest [5 and 10 mm (12 h)−1] amounts of precipitation.
Finally, the EPS performance during wintertime is analyzed. Two issues are investigated: the seasonal variability of the forecast skill of the new EPS, and the impact of the system upgrade on the wintertime performance. The comparison of the performance of the new EPS system during winter 1996/97 and during summer 1997 indicates that the EPS is more skillful during winter than during summer, with differences in predictive skill around 3 days for precipitation amounts larger than 2 mm (12 h)−1. The comparison of the EPS performance before and after the system upgrade on 10 December 1996 during winter confirms the summer conclusion that the upgraded system is more skillful than the old one.
Abstract
Substantial changes were made to the ECMWF model in May 1985. The extensive revisions to the physical parameterizations were designed to improve the treatment of the large-scale flow in the tropics. In addition, the resolution was increased substantially to a triangular truncation at T106. The purpose of this paper is to evaluate the performance of the new forecasting system on the analysis and forecasting of easterly waves and their associated tropical storms over Africa and the tropical Atlantic.
A wave history generated for the months of August and September 1985 with use of operational analyses and METEOSAT imagery provides the framework for evaluating the performance of the analysis system. The difficulties caused by lack of data are discussed. Shortcomings of the analysis system are illustrated using an example of a short-scale disturbance with a marked convergence line. On the other hand, examples are also presented demonstrating the ability of the analysis system to make sense of widely scattered observations.
The maxima in the vorticity field provide a set of useful markers to track the easterly waves, both in the analyses and in the forecasts. The 48-h forecasts of the positions and intensities of the vorticity maxima are verified those cases for which there is sufficient observational data to have confidence in the low-level wind analysis. The verification results are quite encouraging.
A particular feature of the paper is the series of synoptic studies of the four waves which gave rise to named storms (Danny, Elena, and Gloria) during the period.
Abstract
Substantial changes were made to the ECMWF model in May 1985. The extensive revisions to the physical parameterizations were designed to improve the treatment of the large-scale flow in the tropics. In addition, the resolution was increased substantially to a triangular truncation at T106. The purpose of this paper is to evaluate the performance of the new forecasting system on the analysis and forecasting of easterly waves and their associated tropical storms over Africa and the tropical Atlantic.
A wave history generated for the months of August and September 1985 with use of operational analyses and METEOSAT imagery provides the framework for evaluating the performance of the analysis system. The difficulties caused by lack of data are discussed. Shortcomings of the analysis system are illustrated using an example of a short-scale disturbance with a marked convergence line. On the other hand, examples are also presented demonstrating the ability of the analysis system to make sense of widely scattered observations.
The maxima in the vorticity field provide a set of useful markers to track the easterly waves, both in the analyses and in the forecasts. The 48-h forecasts of the positions and intensities of the vorticity maxima are verified those cases for which there is sufficient observational data to have confidence in the low-level wind analysis. The verification results are quite encouraging.
A particular feature of the paper is the series of synoptic studies of the four waves which gave rise to named storms (Danny, Elena, and Gloria) during the period.
Abstract
We present the results of a series of forecasts on seven weather situations from February 1976 using two models which differ only in their physical parameterizations.
One set of parameterizations was developed at the Geophysical Fluid Dynamics Laboratory (GFDL) some years ago, the other more recently at the European Centre for Medium Range Forcasts (ECMWF). The resolution of the model (N48, 15 levels) was that which ECMWF has used in the first phase of operations, which began in August 1979. The particular aim of the experiments was to study the importance of the differences in the parameterization schemes for the model; in addition, we obtained a general view of the forecast results that might be available in the first phase of operations.
Both sets of parameterizations gave similar results in terms of forecast quality. When measured by the standard objective methods, the range of predictability was 5–6 days. A study of the systematic errors in the forecasts showed that these were mainly associated with a loss of energy in the largest waves; the evolution of these systematic errors appeared to be roughly linear in time. (This is not to say that the systematic errors arise due to linear mechanisms.) A study of the energetics showed that the major part of the loss of energy in the long waves was due to a failure to maintain the stationary part of the long-wave energy. Regarding transient phenomena, the downstream intensification of baroclinic waves appeared sometimes to be predictable beyond 6 days.
Abstract
We present the results of a series of forecasts on seven weather situations from February 1976 using two models which differ only in their physical parameterizations.
One set of parameterizations was developed at the Geophysical Fluid Dynamics Laboratory (GFDL) some years ago, the other more recently at the European Centre for Medium Range Forcasts (ECMWF). The resolution of the model (N48, 15 levels) was that which ECMWF has used in the first phase of operations, which began in August 1979. The particular aim of the experiments was to study the importance of the differences in the parameterization schemes for the model; in addition, we obtained a general view of the forecast results that might be available in the first phase of operations.
Both sets of parameterizations gave similar results in terms of forecast quality. When measured by the standard objective methods, the range of predictability was 5–6 days. A study of the systematic errors in the forecasts showed that these were mainly associated with a loss of energy in the largest waves; the evolution of these systematic errors appeared to be roughly linear in time. (This is not to say that the systematic errors arise due to linear mechanisms.) A study of the energetics showed that the major part of the loss of energy in the long waves was due to a failure to maintain the stationary part of the long-wave energy. Regarding transient phenomena, the downstream intensification of baroclinic waves appeared sometimes to be predictable beyond 6 days.
Abstract
Since 1979, sensors on board the National Oceanic and Atmospheric Administration (NOAA) series of polar meteorological satellites have provided continuous measurements of the earth's surface and atmosphere. One of these sensors, the Television Infrared Observational Satellite (TIROS-N) Operational Vertical Sounder (TOVS), observes earth-emitted radiation in the infrared—with the High-Resolution Infrared Sounder (HIRS)—and in the microwave—with the Microwave Sounding Unit (MSU)—portions of the spectrum. The NOAA and National Aeronautics and Space Administration (NASA) Pathfinder program was designed to make these data more readily accessible to the community in the form of processed geophysical variables (temperature, water vapor, cloud characteristics, and so on) through the “interpretation” of the infrared and microwave radiances. All presently developed interpretation algorithms more or less directly rely on the comparison between a set of observed and a set of simulated radiances. For that reason, the accuracy of the simulation directly influences that of the interpretation of radiances in terms of thermodynamic variables. Comparing simulations to observations is the key to a better knowledge of the main sources of errors affecting either the former or the latter. Instrumental radiometric problems, radiosonde, surface data, and forward radiative transfer model limitations as well as difficulties raised by differences in space and in time of satellite and radiosonde observations (collocations) have long been studied in detail. Less attention has been paid to errors, presumed negligible, generated by the absence of consideration of main absorbing gases (CO2, N2O, CO, O3, and so on) atmospheric seasonal cycles and/or annual trends. In this paper, all important sources of variability of the observations and of the simulations are first reviewed. Then it is shown that analyzing, at different timescales (seasonal, annual), the departures between simulated and observed NOAA TOVS brightness temperatures reveals signatures of these greenhouse gases' concentration variations. Not only the shape of the seasonal variations (locations of the peaks) is in good agreement with what is presently known, but also their amplitude (peak-to-peak) matches relatively well the values predicted from a line-by-line radiative transfer model. Moreover, annual trends correspond very well with the known increase in concentration of gases such as CO2 or N2O, as a result of human activities. Limits of such an analysis are discussed: the most significant one finds its origin in the modest spectral resolution of the TOVS channels that integrate signatures from several absorbers and from many atmospheric layers. However, results from this work leave some hope to extract from these channels interesting information on CO2, N2O, and CO distributions. These results also strengthen the hope to improve greatly the knowledge of the global distribution of a variety of radiatively active gases with the coming second generation of vertical sounders such as NASA's Advanced Infrared Radiation Sounder (AIRS) or the CNES/Eumetsat Infrared Atmospheric Sounder Interferometer (IASI), both characterized by a much higher spectral resolution.
Abstract
Since 1979, sensors on board the National Oceanic and Atmospheric Administration (NOAA) series of polar meteorological satellites have provided continuous measurements of the earth's surface and atmosphere. One of these sensors, the Television Infrared Observational Satellite (TIROS-N) Operational Vertical Sounder (TOVS), observes earth-emitted radiation in the infrared—with the High-Resolution Infrared Sounder (HIRS)—and in the microwave—with the Microwave Sounding Unit (MSU)—portions of the spectrum. The NOAA and National Aeronautics and Space Administration (NASA) Pathfinder program was designed to make these data more readily accessible to the community in the form of processed geophysical variables (temperature, water vapor, cloud characteristics, and so on) through the “interpretation” of the infrared and microwave radiances. All presently developed interpretation algorithms more or less directly rely on the comparison between a set of observed and a set of simulated radiances. For that reason, the accuracy of the simulation directly influences that of the interpretation of radiances in terms of thermodynamic variables. Comparing simulations to observations is the key to a better knowledge of the main sources of errors affecting either the former or the latter. Instrumental radiometric problems, radiosonde, surface data, and forward radiative transfer model limitations as well as difficulties raised by differences in space and in time of satellite and radiosonde observations (collocations) have long been studied in detail. Less attention has been paid to errors, presumed negligible, generated by the absence of consideration of main absorbing gases (CO2, N2O, CO, O3, and so on) atmospheric seasonal cycles and/or annual trends. In this paper, all important sources of variability of the observations and of the simulations are first reviewed. Then it is shown that analyzing, at different timescales (seasonal, annual), the departures between simulated and observed NOAA TOVS brightness temperatures reveals signatures of these greenhouse gases' concentration variations. Not only the shape of the seasonal variations (locations of the peaks) is in good agreement with what is presently known, but also their amplitude (peak-to-peak) matches relatively well the values predicted from a line-by-line radiative transfer model. Moreover, annual trends correspond very well with the known increase in concentration of gases such as CO2 or N2O, as a result of human activities. Limits of such an analysis are discussed: the most significant one finds its origin in the modest spectral resolution of the TOVS channels that integrate signatures from several absorbers and from many atmospheric layers. However, results from this work leave some hope to extract from these channels interesting information on CO2, N2O, and CO distributions. These results also strengthen the hope to improve greatly the knowledge of the global distribution of a variety of radiatively active gases with the coming second generation of vertical sounders such as NASA's Advanced Infrared Radiation Sounder (AIRS) or the CNES/Eumetsat Infrared Atmospheric Sounder Interferometer (IASI), both characterized by a much higher spectral resolution.
Abstract
The purpose of this paper is to demonstrate the ability of a modern data assimilation system to provide long-term diagnostic facilities to monitor the performance of the observational network. Operational data assimilation systems use short-range forecasts to provide the background, or first-guess, field for the analysis. We make a detailed study of the apparent or perceived error of these forecasts when they are verified against radiosondes. On the assumption that the observational error of the radiosondes is horizontally uncorrelated, the perceived forecast error can be partitioned into prediction error, which is horizontally correlated, and observation error, which is not. The calculations show that in areas where there is adequate radiosonde coverage, the 6-hour prediction error is comparable with the observation error.
This statement is discussed from a number of viewpoints. We demonstrate in the Northern Hemisphere midlatitudes, for example, that the forecasts account for most of the evolution of the atmospheric state from one analysis to the next, so that the analysis algorithm needs to make only a small correction to an accurate first-guess field; the situation is rather different in the Southern Hemisphere. If the doubling time for small errors is two days, then analysis error will amplify by less than 10% in 6 hours.
This being the case, the statistics of the forecast/observation differences have a simple statistical structure. Large variations of the statistics from station to station, or large biases, are indicative of problems in the data or in the assimilation system. Case studies demonstrate the ability of simple statistical tools to identify systematically erroneous radiosonde wind data in data sparse, as well as in data rich areas, errors which would have been difficult to detect in any other way. The statistical tools are equally effective in diagnosing the performance of the assimilation system.
The results suggest that it is possible to provide regular feedback on the quality of observations of winds and heights to operators of radiosonde networks and other observational systems. This capability has become available over the last decade through improvements in the techniques of numerical weather analysis and prediction.
Abstract
The purpose of this paper is to demonstrate the ability of a modern data assimilation system to provide long-term diagnostic facilities to monitor the performance of the observational network. Operational data assimilation systems use short-range forecasts to provide the background, or first-guess, field for the analysis. We make a detailed study of the apparent or perceived error of these forecasts when they are verified against radiosondes. On the assumption that the observational error of the radiosondes is horizontally uncorrelated, the perceived forecast error can be partitioned into prediction error, which is horizontally correlated, and observation error, which is not. The calculations show that in areas where there is adequate radiosonde coverage, the 6-hour prediction error is comparable with the observation error.
This statement is discussed from a number of viewpoints. We demonstrate in the Northern Hemisphere midlatitudes, for example, that the forecasts account for most of the evolution of the atmospheric state from one analysis to the next, so that the analysis algorithm needs to make only a small correction to an accurate first-guess field; the situation is rather different in the Southern Hemisphere. If the doubling time for small errors is two days, then analysis error will amplify by less than 10% in 6 hours.
This being the case, the statistics of the forecast/observation differences have a simple statistical structure. Large variations of the statistics from station to station, or large biases, are indicative of problems in the data or in the assimilation system. Case studies demonstrate the ability of simple statistical tools to identify systematically erroneous radiosonde wind data in data sparse, as well as in data rich areas, errors which would have been difficult to detect in any other way. The statistical tools are equally effective in diagnosing the performance of the assimilation system.
The results suggest that it is possible to provide regular feedback on the quality of observations of winds and heights to operators of radiosonde networks and other observational systems. This capability has become available over the last decade through improvements in the techniques of numerical weather analysis and prediction.
