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Eugenia Kalnay

University of Maryland, College Park

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Takemasa Miyoshi

RIKEN Center for Computational Science

Istvan Szunyogh

Texas A&M University

Brian R. Hunt

Victoria University

W. E. Baker

National Oceanic and Atmospheric Administration

Edward Ott

University of Maryland, College Park

Eric J. Kostelich

Arizona State University

Kayo Ide

University of Maryland, College Park

Safa Motesharrei

University of Maryland, College Park

James A. Yorke

University of Maryland, College Park

Robert Atlas

NOAA Atlantic Oceanographic and Meteorological Laboratories

Cooperative Institutions

University of Maryland, College Park

National Oceanic and Atmospheric Administration

Goddard Space Flight Center

NOAA National Centers for Environmental Prediction

NSF National Center for Atmospheric Research

NOAA Center for Satellite Applications and Research

NOAA National Weather Service

European Centre for Medium-Range Weather Forecasts

NOAA Environmental Modeling Center

Earth System Science Interdisciplinary Center

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Rotating Stratified Flow over Finite Isolated Topography

Journal of the Atmospheric Sciences (1976)

Lee-Or Merkine Eugenia Kalnay

Inviscid, steady, stratified rotating flow over a finite, isolated topographic feature is critically analyzed. The formulation is based on approximating the horizontal momentum by the geostrophic momentum. A boundary value problem govern the perturbation pressure field. The solution is an anticyclonic, topographically-bound vortex whose characteristics are independent of the upstream velocity but do depend on stratification, rotation, and the nature of the topography. The vortex is baroclinic in the vicinity of the mountain but barotropic in the far field. The velocity field is a combination of the bound vortex and an up-stream velocity interacting with the perturbation pressure field. The effects of stratification, upstream velocity and the nature of the topography are investigated, and fluid trajectories are plotted.

Barotropic fluid

Inviscid flow

Stratification (seeds)

Stratified flows

Pressure gradient

10.1175/1520-0469(1976)033<0908:rsfofi>2.0.co;2

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Citations (29)

SatERR: A Community Error Inventory for Satellite Microwave Observation Error Representation and Uncertainty Quantification

Bulletin of the American Meteorological Society (2023)

John Xun Yang Yalei You William J. Blackwell Cheng Da Eugenia Kalnay

Abstract Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT.

Ground truth

Errors-in-Variables Models

10.1175/bams-d-22-0207.1

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Estimation of Surface Fluxes of Heat, Moisture, and Momentum from Atmospheric Data Assimilation

한국기상학회 학술대회 논문집 (2013)

Ji-Sun Kang Eugenia Kalnay

Momentum (technical analysis)

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Historical overview of numerical weather prediction

Cambridge University Press eBooks (2002)

This comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear description of numerical methods, and the determination of initial conditions using weather observations (an important science known as data assimilation). Finally, this book provides a clear discussion of the problems of predictability and chaos in dynamical systems and how they can be applied to atmospheric and oceanic systems. Professors and students in meteorology, atmospheric science, oceanography, hydrology and environmental science will find much to interest them in this book, which can also form the basis of one or more graduate-level courses.

Predictability

Weather prediction

10.1017/cbo9780511802270.002

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Citations (2)

Assimilation precipitation with Local Ensemble Transform Kalman Filter

J. Liu Eugenia Kalnay

Assimilation (phonology)

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Operational Ensemble Prediction at the National Meteorological Center: Practical Aspects

Weather and Forecasting (1993)

M. Steven Tracton Eugenia Kalnay

On 7 December 1992 NMC began operational ensemble prediction. The ensemble configuration provides 14 independent forecasts every day, verifying on days 1 through 10. The ensemble members are generated through a combination of time lagging [Lagged-Average Forecasting] and a new method, Breeding of Growing Modes (Toth and Kalnay). In adopting the ensemble approach, NMC explicitly recognizes that forecasts are stochastic, not deterministic, in nature. There is no single solution, only an array of possibilities, and forecast ensembles provide a rational basis for assessing the range and likelihood of alternative scenarios. Given the near saturation of computer resources at NMC, implementation of ensemble prediction required a trade-off between model resolution and multiple runs. Before 7 December 1992, NMC was producing a single global forecast through 10 days with the highest-resolution (T126) version possible of its medium-range forecast model. Now, based an experiments that showed no adverse impact upon the quality of forecasts, the T126 model run is truncated to T62 resolution beyond day 6. The computer savings are used to generate the balance of the ensemble members at the lower T62 resolution. While these complementary runs are, on the average, somewhat less skillful when considered individually, it is expected that ensemble averaging will increase skill levels. More importantly, we expect that ensemble prediction will enhance the utility of NWP by (a) providing a basis for the estimation of the reliability of forecasts, and (b) creating a quantitative foundation for probabilistic forecasting. A major challenge of ensemble prediction is to condense the large amounts of information provided by ensembles into a user-friendly format that can be easily assimilated and used by forecasters. Some examples of output products relevant to operational forecast applications are illustrated. They include the display of each member of the ensemble, ensemble mean and dispersion fields, "clustering" of similar forecasts, and simple probability estimates. While this implementation of ensemble prediction is relatively modest (ensembles of 14 members for the forecasts encompassing days 1 through 10), it does provide the basis for development of operational experience with ensemble forecasting, and for research directed toward maximizing the utility of NMC's numerical guidance.

Ensemble forecasting

Ensemble average

Probabilistic Forecasting

Lagging

Forecast verification

Ensemble Learning

Consensus forecast

10.1175/1520-0434(1993)008<0379:oepatn>2.0.co;2

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Citations (328)

The relative importance of mass and wind data in the FGGE observing system

Eugenia Kalnay J. C. Jusem James Pfaendtner

The use of mass and wind data in numerical weather prediction is examined. The applicability of the mass and wind data on the skill of numerical weather prediction is evaluated by real data assimilation experiments using the the NASA/Goddard Laboratory for Atmospheres analysis/forecast system of Baker (1983) and Kalnay et al. (1983). It is observed that the wind observations are important for small scales and in the tropics and that the wind observations are more accurate than mass observations.

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Citations (9)

Bulletin of the American Meteorological Society, Volume 74, Number 12, December 1993

Bulletin of the American Meteorological Society (1993)

Zoltán Tóth Eugenia Kalnay

Alden makes it easy to get all the NEXRAD data available from the NWS through its NEXRAD Information Dissemination Service (NIDS).Alden's ALRAD software, allows you to receive the entire suite of NIDS products using most 386/486 PCs operating under DOS.ALRAD is the reliable way to get NEXRAD data for one low monthly fee.

10.1175/1520-0477-74.12.fmi

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Medium Range Lagged Average Forecasts

Monthly Weather Review (1988)

A. Dalcher Eugenia Kalnay Ross N. Hoffman

In this work we report the application of the lagged average forecasting (LAF) technique to operational forecasts of the ECMWF. The ECMWF data consist of two 100-day samples of 10-day forecasts of 500 mb geopotential height for winter 1980/81 and summer 1981. The LAF ensemble includes the latest operational forecast, and also forecasts for the same verification time stated one or more days earlier than the latest one. We focus on the following two issues: 1) Does ensemble averaging improve forecast skill and 2) Is the dispersion of the ensemble useful in predicting forecast skill. We used the LAF technique to produce 3, 5, 7, 8 and 9 day forecasts of the 500 mb height field. The results show the statistically filtered LAF is a marked improvement upon the operational forecast after 5 days. We find that on a global scale forecast skill is weakly correlated with the dispersion of the ensemble, as measured by the rms difference between the operational forecast and the statistically filtered LAF.

Geopotential height

Forecast verification

Consensus forecast

Geopotential

Ensemble average

Synoptic scale meteorology

Quantitative precipitation forecast

10.1175/1520-0493(1988)116<0402:mrlaf>2.0.co;2

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Citations (70)

Ensemble-based observation impact estimates using the NCEP GFS

Tellus A Dynamic Meteorology and Oceanography (2013)

Yoichiro Ota John Derber Eugenia Kalnay Takemasa Miyoshi

The impacts of the assimilated observations on the 24-hour forecasts are estimated with the ensemble-based method proposed by Kalnay et al. using an ensemble Kalman filter (EnKF). This method estimates the relative impact of observations in data assimilation similar to the adjoint-based method proposed by Langland and Baker but without using the adjoint model. It is implemented on the National Centers for Environmental Prediction Global Forecasting System EnKF that has been used as part of operational global data assimilation system at NCEP since May 2012. The result quantifies the overall positive impacts of the assimilated observations and the relative importance of the satellite radiance observations compared to other types of observations, especially for the moisture fields. A simple moving localisation based on the average wind, although not optimal, seems to work well. The method is also used to identify the cause of local forecast failure cases in the 24-hour forecasts. Data-denial experiments of the observations identified as producing a negative impact are performed, and forecast errors are reduced as estimated, thus validating the impact estimation.

10.3402/tellusa.v65i0.20038

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Citations (70)