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Abstract
A new methodology for the combination of active and passive microwave measurements for near-surface precipitation retrieval from the Tropical Rainfall Measuring Mission (TRMM) data was developed. The approach consists of a stand-alone passive microwave algorithm that is calibrated by collocated radar estimates. The passive microwave technique was based on combined cloud model–radiative transfer simulations including varying surface conditions, a melting layer parameterization, and approximative three-dimensional radiative transfer. The representativeness of the simulations with respect to the TRMM Microwave Imager (TMI) observations was evaluated replacing brightness temperatures by empirical orthogonal functions. Thus, nine TMI correlated channels may be replaced by two to three empirical orthogonal functions representating 97%–98% of total variability. Comparing the principal components to those from TMI observations containing precipitation revealed that the 85.5-GHz brightness temperatures from the simulations represent the major source of mismatch. This is due to the accumulation of uncertainties in cloud model parameterizations of ice microphysics and approximative radiative transfer at this frequency where scattering is most efficient. Depending on the lowest detectable rainfall threshold, the simulations covered 88%–99% of observations from collocated TMI–precipitation radar measurements. Gaps occurred mostly for less intense cloud systems that are not well represented by the cloud model simulations. The ambiguity of observations, that is, the multiplicity of hydrometeor profiles with the same passive microwave signature, was also analyzed. It was found that ambiguity decreases with increasing intensity of the observed scene. In terms of near-surface rain liquid water content, the standard deviation reaches 50%–100% for less intense rain (0.01 g m−3) and is reduced to 20%–30% for intense rain (1.0 g m−3) events. Excluding the 85.5-GHz channels clearly produced less ambiguity. About 80%–95% of all cases showed less than 50% standard deviation of the retrieval variable per database entry compared to 65%–85% when the 85.5-GHz channels were included.
Abstract
A new methodology for the combination of active and passive microwave measurements for near-surface precipitation retrieval from the Tropical Rainfall Measuring Mission (TRMM) data was developed. The approach consists of a stand-alone passive microwave algorithm that is calibrated by collocated radar estimates. The passive microwave technique was based on combined cloud model–radiative transfer simulations including varying surface conditions, a melting layer parameterization, and approximative three-dimensional radiative transfer. The representativeness of the simulations with respect to the TRMM Microwave Imager (TMI) observations was evaluated replacing brightness temperatures by empirical orthogonal functions. Thus, nine TMI correlated channels may be replaced by two to three empirical orthogonal functions representating 97%–98% of total variability. Comparing the principal components to those from TMI observations containing precipitation revealed that the 85.5-GHz brightness temperatures from the simulations represent the major source of mismatch. This is due to the accumulation of uncertainties in cloud model parameterizations of ice microphysics and approximative radiative transfer at this frequency where scattering is most efficient. Depending on the lowest detectable rainfall threshold, the simulations covered 88%–99% of observations from collocated TMI–precipitation radar measurements. Gaps occurred mostly for less intense cloud systems that are not well represented by the cloud model simulations. The ambiguity of observations, that is, the multiplicity of hydrometeor profiles with the same passive microwave signature, was also analyzed. It was found that ambiguity decreases with increasing intensity of the observed scene. In terms of near-surface rain liquid water content, the standard deviation reaches 50%–100% for less intense rain (0.01 g m−3) and is reduced to 20%–30% for intense rain (1.0 g m−3) events. Excluding the 85.5-GHz channels clearly produced less ambiguity. About 80%–95% of all cases showed less than 50% standard deviation of the retrieval variable per database entry compared to 65%–85% when the 85.5-GHz channels were included.
Abstract
The one- plus four-dimensional variational data assimilation (“1D+4DVAR”) method currently run in operations at ECMWF with rain-affected radiances from the Special Sensor Microwave Imager is used to study the potential impact of assimilating NCEP stage-IV analyses of hourly accumulated surface precipitation over the U.S. mainland. These data are a combination of rain gauge measurements and observations from the high-resolution Doppler Next-Generation Weather Radars. Several 1D+4DVAR experiments have been run over a month in spring 2005. First, the quality of the precipitation forecasts in the control experiment is assessed. Then, it is shown that the impact of the assimilation of the additional rain observations on global scores of dynamical fields and temperature is rather neutral, while precipitation scores are improved for forecast ranges up to 12 h. Additional 1D+4DVAR experiments in which all moisture-affected observations are removed over the United States demonstrate that the NCEP stage-IV precipitation data on their own can clearly be beneficial to the analyses and subsequent forecasts of the moisture field. This result suggests that the potential impact of precipitation observations is overshadowed by the influence of other high-quality humidity observations, in particular, radiosondes. It also confirms that the assimilation of precipitation observations has the ability to improve the quality of moisture analyses and forecasts in data-sparse regions. Finally, the limitations inherent in the current assimilation of precipitation data, their implications for the future, and possible ways of improvement are discussed.
Abstract
The one- plus four-dimensional variational data assimilation (“1D+4DVAR”) method currently run in operations at ECMWF with rain-affected radiances from the Special Sensor Microwave Imager is used to study the potential impact of assimilating NCEP stage-IV analyses of hourly accumulated surface precipitation over the U.S. mainland. These data are a combination of rain gauge measurements and observations from the high-resolution Doppler Next-Generation Weather Radars. Several 1D+4DVAR experiments have been run over a month in spring 2005. First, the quality of the precipitation forecasts in the control experiment is assessed. Then, it is shown that the impact of the assimilation of the additional rain observations on global scores of dynamical fields and temperature is rather neutral, while precipitation scores are improved for forecast ranges up to 12 h. Additional 1D+4DVAR experiments in which all moisture-affected observations are removed over the United States demonstrate that the NCEP stage-IV precipitation data on their own can clearly be beneficial to the analyses and subsequent forecasts of the moisture field. This result suggests that the potential impact of precipitation observations is overshadowed by the influence of other high-quality humidity observations, in particular, radiosondes. It also confirms that the assimilation of precipitation observations has the ability to improve the quality of moisture analyses and forecasts in data-sparse regions. Finally, the limitations inherent in the current assimilation of precipitation data, their implications for the future, and possible ways of improvement are discussed.
Abstract
A comparison of global model cloud and rain parameterization output with satellite observed radiances was carried out. Hydrometeor profiles from ECMWF operational short-range forecasts were combined with a microwave radiative transfer model to generate observation-equivalent radiances simulating the Special Sensor Microwave Imager (SSM/I) measurements. These were generated for two 15-day periods in January and July 2001 to be compared to SSM/I observations from three DMSP satellites, namely F-13, F-14, and F-15. The simulations were analyzed to isolate the relative contributions of water vapor, cloud water, rain, and snow to the total signal given their frequency of occurrence in the global fields. The 19.35-GHz channel has the great advantage of being less sensitive to cloud geometry and model-generated snow, thus providing a more unique relationship between cloud–rainwater and blackbody equivalent brightness temperatures (TBs). The 37.0-GHz channel showed great skill in separating cloud and (moderate to heavy) rainfall. The uncertainties in cloud geometry and ice microphysics inhibit an interpretation of 85.5-GHz brightness temperatures.
The evaluation was based on 1) the calculation of cloud and rain occurrence applying the same TB threshold screening to both observations and simulation, and 2) the analysis of global TB histograms for clouds and precipitation. From the first part, the model tendency to produce too large cloud and rain systems was identified. While some smaller-scale cloud features are missing, the onset of condensation generally produces larger systems than observed. Since the precipitation scheme is diagnostic, the cloud scheme propagates this problem to the rain coverage. With the results from the second part, the overestimation of extent and intensity was quantified to ≈10–15 K at 19.35 and ≈15–30 K at 37.0 GHz at horizontal polarization.
This was consistent with a direct estimation of retrieved liquid water paths using a variational retrieval scheme and of rainfall rates from a parametric algorithm. The globally averaged liquid water path from the model's first guess was about 75% higher than that from the retrievals, while globally averaged rain rate was 160% higher than retrieved. The major contribution to this overestimation originated from the Tropics, suggesting the convection scheme and/or its inputs as a major source of overestimation.
Abstract
A comparison of global model cloud and rain parameterization output with satellite observed radiances was carried out. Hydrometeor profiles from ECMWF operational short-range forecasts were combined with a microwave radiative transfer model to generate observation-equivalent radiances simulating the Special Sensor Microwave Imager (SSM/I) measurements. These were generated for two 15-day periods in January and July 2001 to be compared to SSM/I observations from three DMSP satellites, namely F-13, F-14, and F-15. The simulations were analyzed to isolate the relative contributions of water vapor, cloud water, rain, and snow to the total signal given their frequency of occurrence in the global fields. The 19.35-GHz channel has the great advantage of being less sensitive to cloud geometry and model-generated snow, thus providing a more unique relationship between cloud–rainwater and blackbody equivalent brightness temperatures (TBs). The 37.0-GHz channel showed great skill in separating cloud and (moderate to heavy) rainfall. The uncertainties in cloud geometry and ice microphysics inhibit an interpretation of 85.5-GHz brightness temperatures.
The evaluation was based on 1) the calculation of cloud and rain occurrence applying the same TB threshold screening to both observations and simulation, and 2) the analysis of global TB histograms for clouds and precipitation. From the first part, the model tendency to produce too large cloud and rain systems was identified. While some smaller-scale cloud features are missing, the onset of condensation generally produces larger systems than observed. Since the precipitation scheme is diagnostic, the cloud scheme propagates this problem to the rain coverage. With the results from the second part, the overestimation of extent and intensity was quantified to ≈10–15 K at 19.35 and ≈15–30 K at 37.0 GHz at horizontal polarization.
This was consistent with a direct estimation of retrieved liquid water paths using a variational retrieval scheme and of rainfall rates from a parametric algorithm. The globally averaged liquid water path from the model's first guess was about 75% higher than that from the retrievals, while globally averaged rain rate was 160% higher than retrieved. The major contribution to this overestimation originated from the Tropics, suggesting the convection scheme and/or its inputs as a major source of overestimation.
Abstract
Data storage and data processing generate significant cost for weather and climate modeling centers. The volume of data that needs to be stored and data that are disseminated to end users increases with increasing model resolution and the use of larger forecast ensembles. If precision of data is reduced, cost can be reduced accordingly. In this paper, three new methods to allow a reduction in precision with minimal loss of information are suggested and tested. Two of these methods rely on the similarities between ensemble members in ensemble forecasts. Therefore, precision will be high at the beginning of forecasts when ensemble members are more similar, to provide sufficient distinction, and decrease with increasing ensemble spread. To keep precision high for predictable situations and low elsewhere appears to be a useful approach to optimize data storage in weather forecasts. All methods are tested with data of operational weather forecasts of the European Centre for Medium-Range Weather Forecasts.
Abstract
Data storage and data processing generate significant cost for weather and climate modeling centers. The volume of data that needs to be stored and data that are disseminated to end users increases with increasing model resolution and the use of larger forecast ensembles. If precision of data is reduced, cost can be reduced accordingly. In this paper, three new methods to allow a reduction in precision with minimal loss of information are suggested and tested. Two of these methods rely on the similarities between ensemble members in ensemble forecasts. Therefore, precision will be high at the beginning of forecasts when ensemble members are more similar, to provide sufficient distinction, and decrease with increasing ensemble spread. To keep precision high for predictable situations and low elsewhere appears to be a useful approach to optimize data storage in weather forecasts. All methods are tested with data of operational weather forecasts of the European Centre for Medium-Range Weather Forecasts.
Abstract
The assimilation of cloud- and rain-affected radiances in numerical weather prediction systems requires fast and accurate radiative transfer models. One of the largest sources of modeling errors originates from the assumptions regarding the vertical and horizontal subgrid-scale variability of model clouds and precipitation. In this work, cloud overlap assumptions are examined in the context of microwave radiative transfer and used to develop an accurate reference model. A fast cloud overlap algorithm is presented that allows for the accurate simulation of microwave radiances with a small number of radiative transfer calculations. In particular, the errors for a typical two-column approach currently used operationally are found to be relatively large for many cases of cloudy fields containing precipitation, even those with an overall cloud fraction of unity; these errors are largely eliminated by using the new approach presented here, at the cost of a slight increase in computation time. Radiative transfer cloud overlap errors are also evident in simulations when compared to actual satellite observations, in that the biases are somewhat reduced when applying a more accurate treatment of cloud overlap.
Abstract
The assimilation of cloud- and rain-affected radiances in numerical weather prediction systems requires fast and accurate radiative transfer models. One of the largest sources of modeling errors originates from the assumptions regarding the vertical and horizontal subgrid-scale variability of model clouds and precipitation. In this work, cloud overlap assumptions are examined in the context of microwave radiative transfer and used to develop an accurate reference model. A fast cloud overlap algorithm is presented that allows for the accurate simulation of microwave radiances with a small number of radiative transfer calculations. In particular, the errors for a typical two-column approach currently used operationally are found to be relatively large for many cases of cloudy fields containing precipitation, even those with an overall cloud fraction of unity; these errors are largely eliminated by using the new approach presented here, at the cost of a slight increase in computation time. Radiative transfer cloud overlap errors are also evident in simulations when compared to actual satellite observations, in that the biases are somewhat reduced when applying a more accurate treatment of cloud overlap.
Abstract
The retrieval errors of cloud and precipitation hydrometeor contents from spaceborne observations are estimated at microwave frequencies in atmospheric windows between 18 and 150 GHz and in oxygen absorption complexes near 50–60 and 118 GHz. The method is based on a variational retrieval framework using a priori information on the cloud, atmosphere, and surface states from ECMWF short-range forecasts under different weather regimes. This approach was chosen because a consistent description of the model state and its uncertainties is provided, which is unavailable for other methods. The results show that the sounding channels provide more stable, more accurate, and less biased retrievals than window channels—in particular, over land surfaces and with regard to snowfall. Average performance estimates showed that if sounding channels are used, 80% of all retrievals are within 100% error limits and 60% of them are within 50% error limits with regard to rainfall. For snowfall, the sounding channels produce 60% of all retrievals with errors below 100% for rates smaller than 1 mm h−1, and 50%–80% of the cases have errors below 50% for more intense snowfall.
Abstract
The retrieval errors of cloud and precipitation hydrometeor contents from spaceborne observations are estimated at microwave frequencies in atmospheric windows between 18 and 150 GHz and in oxygen absorption complexes near 50–60 and 118 GHz. The method is based on a variational retrieval framework using a priori information on the cloud, atmosphere, and surface states from ECMWF short-range forecasts under different weather regimes. This approach was chosen because a consistent description of the model state and its uncertainties is provided, which is unavailable for other methods. The results show that the sounding channels provide more stable, more accurate, and less biased retrievals than window channels—in particular, over land surfaces and with regard to snowfall. Average performance estimates showed that if sounding channels are used, 80% of all retrievals are within 100% error limits and 60% of them are within 50% error limits with regard to rainfall. For snowfall, the sounding channels produce 60% of all retrievals with errors below 100% for rates smaller than 1 mm h−1, and 50%–80% of the cases have errors below 50% for more intense snowfall.
No Abstract available.
No Abstract available.
Abstract
Observing system experiments within the operational ECMWF data assimilation framework have been performed for summer 2008 when the largest recorded number of Global Navigation Satellite System (GNSS) radio occultation observations from both operational and experimental satellites were available. Constellations with 0%, 5%, 33%, 67%, and 100% data volume were assimilated to quantify the sensitivity of analysis and forecast quality to radio occultation data volume. These observations mostly constrain upper-tropospheric and stratospheric temperatures and correct an apparent model bias that changes sign across the upper-troposphere–lower-stratosphere boundary. This correction effect does not saturate with increasing data volume, even if more data are assimilated than available in today’s analyses. Another important function of radio occultation data, namely, the anchoring of variational radiance bias corrections, is demonstrated in this study. This effect also does not saturate with increasing data volume. In the stratosphere, the anchoring by radio occultation data is stronger than provided by radiosonde and aircraft observations.
Abstract
Observing system experiments within the operational ECMWF data assimilation framework have been performed for summer 2008 when the largest recorded number of Global Navigation Satellite System (GNSS) radio occultation observations from both operational and experimental satellites were available. Constellations with 0%, 5%, 33%, 67%, and 100% data volume were assimilated to quantify the sensitivity of analysis and forecast quality to radio occultation data volume. These observations mostly constrain upper-tropospheric and stratospheric temperatures and correct an apparent model bias that changes sign across the upper-troposphere–lower-stratosphere boundary. This correction effect does not saturate with increasing data volume, even if more data are assimilated than available in today’s analyses. Another important function of radio occultation data, namely, the anchoring of variational radiance bias corrections, is demonstrated in this study. This effect also does not saturate with increasing data volume. In the stratosphere, the anchoring by radio occultation data is stronger than provided by radiosonde and aircraft observations.
Abstract
The assimilation of observations indicative of quantitative cloud and precipitation characteristics is desirable for improving weather forecasts. For many fundamental reasons, it is a more difficult problem than the assimilation of conventional or clear-sky satellite radiance data. These reasons include concerns regarding nonlinearity of the required observation operators (forward models), nonnormality and large variances of representativeness, retrieval, or observation–operator errors, validation using new measures, dynamic and thermodynamic balances, and possibly limited predictability. Some operational weather prediction systems already assimilate precipitation observations, but much more research and development remains. The apparently critical, fundamental, and peculiar nature of many issues regarding cloud and precipitation assimilation implies that their more careful examination will be required for accelerating progress.
Abstract
The assimilation of observations indicative of quantitative cloud and precipitation characteristics is desirable for improving weather forecasts. For many fundamental reasons, it is a more difficult problem than the assimilation of conventional or clear-sky satellite radiance data. These reasons include concerns regarding nonlinearity of the required observation operators (forward models), nonnormality and large variances of representativeness, retrieval, or observation–operator errors, validation using new measures, dynamic and thermodynamic balances, and possibly limited predictability. Some operational weather prediction systems already assimilate precipitation observations, but much more research and development remains. The apparently critical, fundamental, and peculiar nature of many issues regarding cloud and precipitation assimilation implies that their more careful examination will be required for accelerating progress.
