Can machine learning correct microwave humidity radiances for the influence of clouds?

A methodology based on quantile regression neural networks (QRNNs) is presented that identifies and corrects the cloud impact on microwave humidity sounder radiances at 183 GHz. This approach estimates the posterior distributions of noise-free clear-sky (NFCS) radiances, providing nearly bias-free estimates of clear-sky radiances with a full posterior error distribution. It is first demonstrated by application to a present sensor, the MicroWave Humidity Sounder 2 (MWHS-2); then the applicability to sub-millimetre (sub-mm) sensors is also analysed. The QRNN results improve upon what operational cloud filtering techniques like a scattering index can achieve but are ultimately imperfect due to limited information content on cirrus impact from traditional microwave channels - the negative departures associated with high cloud impact are successfully corrected, but thin cirrus clouds cannot be fully corrected. In contrast, when sub-mm observations are used, QRNN successfully corrects most cases with cloud impact, with only 2 %-6 % of the cases left partially corrected. The methodology works well even if only one sub-mm channel (325 GHz) is available. When using sub-mm observations, cloud correction usually results in error distributions with a standard deviation less than typical channel noise values. Furthermore, QRNN outputs predicted quantiles for case-specific uncertainty estimates, successfully representing the uncertainty of cloud correction for each observation individually. In comparison to deterministic correction or filtering approaches, the corrected radiances and attendant uncertainty estimates have great potential to be used efficiently in assimilation systems due to being largely unbiased and adding little further uncertainty to the measurements

Satellite radiance data assimilation for binary tropical cyclone cases over the western North Pacific

A total of three binary tropical cyclone (TC) cases over the Western North Pacific are selected to investigate the effects of satellite radiance data assimilation on analyses and forecasts of binary TCs. Two parallel cycling experiments with a 6 h interval are performed for each binary TC case, and the difference between the two experiments is whether satellite radiance observations are assimilated. Satellite radiance observations are assimilated using the Weather Research and Forecasting Data Assimilation (WRFDA)'s three-dimensional variational (3D-Var) system, which includes the observation operator, quality control procedures, and bias correction algorithm for radiance observations. On average, radiance assimilation results in slight improvements of environmental fields and track forecasts of binary TC cases, but the detailed effects vary with the case. When there is no direct interaction between binary TCs, radiance assimilation leads to better depictions of environmental fields, and finally it results in improved track forecasts. However, positive effects of radiance assimilation on track forecasts can be reduced when there exists a direct interaction between binary TCs and intensities/structures of binary TCs are not represented well. An initialization method (e.g., dynamic initialization) combined with radiance assimilation and/or more advanced DA techniques (e.g., hybrid method) can be considered to overcome these limitations

Assimilation of all-sky seviri infrared brightness temperatures in a regional-scale ensemble data assimilation system

Ensemble data assimilation experiments were performed to assess the ability of satellite all-sky infrared brightness temperatures and different bias correction (BC) predictors to improve the accuracy of short-range forecasts used as the model background during each assimilation cycle. Satellite observations sensitive to clouds and water vapor in the upper troposphere were assimilated at hourly intervals during a 3-day period. Linear and nonlinear conditional biases were removed from the infrared observations using a Taylor series polynomial expansion of the observation-minus-background departures and BC predictors sensitive to clouds and water vapor or to variations in the satellite zenith angle. Assimilating the all-sky infrared brightness temperatures without BC degraded the forecast accuracy based on comparisons to radiosonde observations. Removal of the linear and nonlinear conditional biases from the satellite observations substantially improved the results, with predictors sensitive to the location of the cloud top having the largest impact, especially when higher order nonlinear BC terms were used. Overall, experiments employing the observed cloud top height or observed brightness temperature as the bias predictor had the smallest water vapor, cloud, and wind speed errors, while also having less degradation to temperatures than occurred when using other predictors. The forecast errors were smaller during these experiments because the cloud-height-sensitive BC predictors were able to more effectively remove the large conditional biases for lower brightness temperatures associated with a deficiency in upper-level clouds in the model background

Nonlinear bias correction for numerical weather prediction models

Data assimilation is an inverse problem that seeks to optimally combine information from a set of observations with a first guess analysis to generate the best estimate of the current state of a dynamic system. It is an essential part of numerical weather prediction because the accuracy of a model forecast is closely tied to the accuracy of the initial conditions. Thus, the goal of this thesis is to enhance our ability to assimilate satellite brightness temperatures through development of bias correction (BC) methods to remove systematic errors from the observations and model background. In the first part of the thesis, we introduce an innovative BC method that uses a Taylor series polynomial expansion of the observation-minus-background (OMB) departures to remove linear and nonlinear conditional biases from all-sky satellite infrared brightness temperatures. Passive monitoring experiments reveal that variables sensitive to the cloud top height are the most effective BC predictors and that higher-order Taylor series terms are necessary to account for complex nonlinear biases in the OMB departures. Active data assimilation experiments using the nonlinear BC method show that the model background is most improved when higher-order cloud-sensitive predictors are employed. Following this work, we use the Lorenz-63 model to develop a model bias estimation method based on an asymptotic expansion of the model dynamics for small time scales and small perturbations in one of its parameters. The model bias estimators are subsequently used to improve the model background error covariance matrix used during the data assimilation step. It is shown that the combination of a static matrix with a dynamic matrix that varies with time leads to more accurate model analyses and forecasts. Together, results from this thesis demonstrate that bias predictors derived from polynomial expansions of modeled and observed variables can improve the performance of data assimilation systems

Status of the Global Observing System for Climate

Status of the Global Observing System for Climat

Proceedings of the 40th Aerospace Mechanisms Symposium

The Aerospace Mechanisms Symposium (AMS) provides a unique forum for those active in the design, production and use of aerospace mechanisms. A major focus is the reporting of problems and solutions associated with the development and flight certification of new mechanisms. Organized by the Mechanisms Education Association, responsibility for hosting the AMS is shared by the National Aeronautics and Space Administration and Lockheed Martin Space Systems Company (LMSSC). Now in its 40th symposium, the AMS continues to be well attended, attracting participants from both the U.S. and abroad. The 40th AMS, hosted by the Kennedy Space Center (KSC) in Cocoa Beach, Florida, was held May 12, 13 and 14, 2010. During these three days, 38 papers were presented. Topics included gimbals and positioning mechanisms, CubeSats, actuators, Mars rovers, and Space Station mechanisms. Hardware displays during the supplier exhibit gave attendees an opportunity to meet with developers of current and future mechanism components. The use of trade names of manufacturers in this publication does not constitute an official endorsement of such products or manufacturers, either expressed or implied, by the National Aeronautics and Space Administratio