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Hydrometeor Identification from GMI Radiometer, Trained Using Polarimetric Radar

Brightness temperatures (TB) from GMI and related sensors contain information about the types and amounts of precipitation particles in a column. Approaches like GPROF or machine learning are good for providing precipitation estimates and other quantified information, but it can be difficult to understand why / how a given set of TBs traces to a given retrieval solution. Outlier TBs that are not well-represented in a training sample can lead to dubious solutions. Our goal is to use the scattering signatures at multiple frequencies to determine whether hail is present, or otherwise graupel, or otherwise snow, or otherwise liquid rain without a substantial precipitation ice component. (A hierarchical approach is employed, so a prediction of hail can include the presence of all other categories, but a prediction of snow implies a lack of graupel or hail, and a prediction of liquid rain implies a lack of any detectable precipitation ice.)

Daniel J. Cecil↗
NASA NTRS2004

Radicals and Reservoirs in the GMI Chemistry and Transport Model: Comparison to Measurements

The most important use of atmospheric chemistry and transport models is to predict the future composition of the atmosphere. The amounts of gases like chlorofluorcarbons, methyl bromide, nitrous oxide and methane are changing and the stratospheric ozone layer will change because these gases are changing. Methyl bromide, nitrous oxide and methane all have natural sources, and also change because of human activity. Chlorofluorcarbons are man-made gases; these are known to decrease stratospheric ozone and future production is banned. They are long-lived gases, and many decades will pass before they are insignificant in the atmosphere. The models are used to predict changes in ozone and other gases; this is a straightforward application. The models must be also tested using observations for the present day atmosphere. This is a challenging task, because the model contains more than 50 species and more than 150 chemical reactions. Data from satellites, ground stations, aircraft and balloons are used to evaluate the model. Different models that are used in international assessments produce different results; in the most recent assessment some predict that ozone will return to 1980 levels by 2025 and others predict that this will not happen until 2050. Since all the parts of the models are conceptually the same, there must be differences in implementation that produce these differences, This work takes a single model, two different sets of winds and temperatures, and repeats the same prediction for the future. Here we compare the results for these two simulations with many observations. The purpose is to identify differences in the model results for the present atmosphere that will lead to different predictions. This sort of controlled comparison will reduce uncertainty in the predictions for stratospheric ozone.

Douglas, Anne R.↗
NASA NTRS2004

Activities of NASA's Global Modeling Initiative (GMI) in the Assessment of Subsonic Aircraft Impact

The Intergovernmental Panel on Climate Change estimated a peak increase in ozone ranging from 7-12 ppbv (zonal and annual average, and relative to a baseline with no aircraft), due to the subsonic aircraft in the year 2015, corresponding to aircraft emissions of 1.3 TgN/year. This range of values presumably reflects differences in model input (e.g., chemical mechanism, ground emission fluxes, and meteorological fields), and algorithms. The model implemented by the Global Modeling Initiative allows testing the impact of individual model components on the assessment calculations. We present results of the impact of doubling the 1995 aircraft emissions of NOx, corresponding to an extra 0.56 TgN/year, utilizing meteorological data from NASA's Data Assimilation Office (DAO), the Goddard Institute for Space Studies (GISS), and the Middle Atmosphere Community Climate Model, version 3 (MACCM3). Comparison of results to observations can be used to assess the model performance. Peak ozone perturbations ranging from 1.7 to 2.2 ppbv of ozone are calculated using the different fields. These correspond to increases in total tropospheric ozone ranging from 3.3 to 4.1 Tg/Os. These perturbations are consistent with the IPCC results, due to the difference in aircraft emissions. However, the range of values calculated is much smaller than in IPCC.

Rodriquez, J. M.↗
NASA NTRS2009

Refactoring and Componentizing Legacy Codes: GMI Case Study

As Earth science applications grow increasingly complex over time, maintenance of such software becomes a significant challenge. In general, the software subsystems were designed, implemented and integrated (over an extended period of time) by different groups that did not follow the same software design standard. The objective was understandably to obtain scientific results quickly, but this goal was often achieved at the expense of good software practices. With that pattern of development, the cost of incremental changes to the applications grow ominously large. This increase in effort, tends to squeeze development resources and largely prevent any systematic attempt to reintroduce better software practices and thereby enhance the maintainability of the software. In this paper, we present the process we used to incrementally refactor and componentize the Global Modeling Initiative code to make it more understandable, maintainable, flexible and extensible. The resulting product not only preserves the scientific integrity of the original code but also extends the capabilities of the code for several applications.

Kouatchou, Jules↗
NASA NTRS2009

Global Precipitation Measurement: GPM Microwave Imager (GMI) Algorithm Development Approach

This slide presentation reviews the approach to the development of the Global Precipitation Measurement algorithm. This presentation includes information about the responsibilities for the development of the algorithm, and the calibration. Also included is information about the orbit, and the sun angle. The test of the algorithm code will be done with synthetic data generated from the Precipitation Processing System (PPS).

Stocker, Erich Franz↗
NASA NTRS2021

Measurements of Rainfall Rate, Drop Size Distribution, and Variability at Middle and Higher Latitudes: Application to the Combined DPR-GMI Algorithm

The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth’s global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager which provide measurements to retrieve the drop size distribution (DSD) and rain rates using a Combined Radar-Radiometer Algorithm (CORRA). Our objective is to validate key assumptions and parameterizations in CORRA and enable improved estimation of precipitation products, especially in the middle-to-higher latitudes in both hemispheres. The DSD parameters and statistical relationships between DSD parameters and radar measurements are a central part of the rainfall retrieval algorithm, which is complicated by regimes where DSD measurements are abysmally sparse (over the open ocean). In view of this, we have assembled optical disdrometer datasets gathered by research vessels, ground stations, and aircrafts to simulate radar observables and validate the scattering lookup tables used in CORRA. The joint use of all DSD datasets spans a large range of drop concentrations and characteristic drop diameters. The scaling normalization of DSDs defines an intercept parameter N(W), which normalizes the concentrations, and a scaling diameter D(m), which compresses or stretches the diameter coordinate axis. A major finding of this study is that a single relationship between N(W) and D(m), on average, unifies all datasets included, from stratocumulus to heavier rainfall regimes. A comparison with the N(W)–D(m) relation used as a constraint in versions 6 and 7 of CORRA highlights the scope for improvement of rainfall retrievals for small drops (D(m) < 1 mm) and large drops (D(m) > 2 mm). The normalized specific attenuation–reflectivity relationships used in the combined algorithm are also found to match well the equivalent relationships derived using DSDs from the three datasets, suggesting that the currently assumed lookup tables are not a major source of uncertainty in the combined algorithm rainfall estimates.

Viswanathan Bringi↗