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Peter Bauer

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.

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Joe Turk and Peter Bauer
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Philippe Lopez and Peter Bauer

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.

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Frédéric Chevallier and Peter Bauer

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.

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Peter D. Düben, Martin Leutbecher, and Peter Bauer

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.

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Peter Bauer, Emmanuel Moreau, and Sabatino Di Michele

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.

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Peter Bauer, Paul Amayenc, Christian D. Kummerow, and Eric A. Smith

Abstract

The objective of this paper is to establish a computationally efficient algorithm making use of the combination of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) observations. To set up the TMI algorithm, the retrieval databases developed in Part I served as input for different inversion techniques: multistage regressions and neural networks as well as Bayesian estimators. It was found that both Bayesian and neural network techniques performed equally well against PR estimates if all TMI channels were used. However, not using the 85.5-GHz channels produced consistently better results. This confirms the conclusions from Part I. Generally, regressions performed worse; thus they seem less suited for general application due to the insufficient representation of the nonlinearities of the TB–rain rate relation. It is concluded that the databases represent the most sensitive part of rainfall algorithm development.

Sensor combination was carried out by gridding PR estimates of rain liquid water content to 27 km × 44 km horizontal resolution at the center of gravity of the TMI 10.65-GHz channel weighting function. A liquid water dependent database collects common samples over the narrow swath covered by both TMI and PR. Average calibration functions are calculated, dynamically updated along the satellite track, and applied to the full TMI swath. The behavior of the calibration function was relatively stable. The TMI estimates showed a slight underestimation of rainfall at low rain liquid water contents (<0.1 g m−3) as well as at very high rainfall intensities (>0.8 g m−3) and excellent agreement in between. The biases were found to not depend on beam filling with a strong correlation to rain liquid water for stratiform clouds that may point to melting layer effects.

The remaining standard deviations between instantaneous TMI and PR estimates after calibration may be treated as a total retrieval error, assuming the PR estimates are unbiased. The error characteristics showed a rather constant absolute error of <0.05 g m−3 for rain liquid water contents <0.1 g m−3. Above, the error increases to 0.6 g m−3 for amounts up to 1 g m−3. In terms of relative errors, this corresponds to a sharp decrease from >100% to 35% between 0.05 and 0.5 g m−3. The database ambiguity, that is, the standard deviation of near-surface rain liquid water contents with the same radiometric signature, provides a means to estimate the contribution from the simulations to this error. In the range where brightness temperatures respond most sensitively to rainwater contents, almost the entire error originates from the ambiguity of signatures. At very low and very high rain rates (<0.05 and >0.7 g m−3) at least half of the total error is explained by the inversion process.

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Christopher W. O’Dell, Peter Bauer, and Ralf Bennartz

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.

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Peter Bauer, George Ohring, Chris Kummerow, and Tom Auligne

No Abstract available.

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Qifeng Lu, William Bell, Peter Bauer, Niels Bormann, and Carole Peubey

Abstract

China’s Feng-Yun-3A (FY-3A), launched in May 2008, is the first in a series of seven polar-orbiting meteorological satellites planned for the next decade by China. The FY-3 series is set to become an important data source for numerical weather prediction (NWP), reanalysis, and climate science. FY-3A is equipped with a microwave temperature sounding instrument (MWTS). This study reports an assessment of the MWTS instrument using the ECMWF NWP model, radiative transfer modeling, and comparisons with equivalent observations from the Advanced Microwave Sounding Unit-A (AMSU-A). The study suggests the MWTS instrument is affected by biases related to large shifts, or errors, in the frequency of the channel passbands as well as radiometer nonlinearity. The passband shifts, relative to prelaunch measurements, are 55, 39, and 33 MHz for channels 2–4, respectively. Relative to the design specification the shifts are 60, 80, and 83 MHz, with uncertainties of ±2.5 MHz. The radiometer nonlinearity results in a positive bias in measured brightness temperatures and is manifested as a quadratic function of measured scene temperatures. By correcting for both of these effects the quality of the MWTS data is improved significantly, with the standard deviations of the (observed minus simulated) differences based on short-range forecast fields reduced by 30%–50% relative to simulations using prelaunch measurements of the passband, to values close to those observed for AMSU-A-equivalent channels. The new methodology could be applied to other microwave temperature sounding instruments and illustrates the value of NWP fields for the on-orbit characterization of satellite sensors.

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