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Michiko Masutani and Ants Leetmaa

Abstract

The link between El Niño and the California wintertime rainfall has been reported in various studies. During the winter of 1994/95, warm sea surface temperature anomalies (SSTAs) were observed in the central Pacific, and widespread significant flooding occurred in California during January 1995 and March 1995. However, the El Niño–Southern Oscillation alone cannot explain the flooding. In March 1995 California suffered flooding after the warm SSTA over the central Pacific had weakened considerably. During November and December, in spite of El Niño conditions, California was not flooded, and more than two standard deviations above normal SSTA in the North Pacific were observed. A possible link between midlatitude warm SSTA and the timing of the onset of flooding is suspected within the seasonal forecasting community.

The climate condition during the northern winter of 1994/95 is described using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis data. Diagnostics show the typical El Niño pattern in the seasonal mean and the link between the position of the jet exit and the flooding over California on the intraseasonal timescale.

The relationship among California floods, the Pacific jet, tropical rainfall, and SSTA is inferred from results of general circulation model (GCM) experiments with various SSTAs. The results show that the rainfall over California is associated with an eastward extension of the Pacific jet, which itself is associated with enhanced tropical convection over the warm SSTA in the central Pacific. The GCM experiments also show that rainfall over the Indian Ocean can contribute to the weakening of the Pacific jet and to dryness over California. The GCM experiments did not show significant impact of North Pacific SSTA, either upon the Pacific jet or upon rainfall over California. The agreement with diagnostics results is discussed. GCM experiments suggest the link between the tropical intraseasonal oscillation (TIO) and the flooding in March in California, since there is a strong TIO component in rainfall over the Indian Ocean.

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Robert E. Livezey, Michiko Masutani, and Ming Ji

The feasibility of using a two-tier approach to provide guidance to operational long-lead seasonal prediction is explored. The approach includes first a forecast of global sea surface temperatures (SSTs) using a coupled general circulation model, followed by an atmospheric forecast using an atmospheric general circulation model (AGCM). For this exploration, ensembles of decade-long integrations of the AGCM driven by observed SSTs and ensembles of integrations of select cases driven by forecast SSTs have been conducted. The ability of the model in these sets of runs to reproduce observed atmospheric conditions has been evaluated with a multiparameter performance analysis.

Results have identified performance and skill levels in the specified SST runs, for winters and springs over the Pacific/North America region, that are sufficient to impact operational seasonal predictions in years with major El Niño–Southern Oscillation (ENSO) episodes. Further, these levels were substantially reproduced in the forecast SST runs for 1-month leads and in many instances for up to one-season leads. In fact, overall the 0- and 1-month-lead forecasts of seasonal temperature over the United States for three falls and winters with major ENSO episodes were substantially better than corresponding official forecasts. Thus, there is considerable reason to develop a dynamical component for the official seasonal forecast process.

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Robert E. Livezey, Michiko Masutani, Ants Leetmaa, Hualan Rui, Ming Ji, and Arun Kumar

Abstract

A prominent year-round ensemble response to a global sea surface temperature (SST) anomaly field fixed to that for January 1992 (near the peak of a major warm El Niño–Southern Oscillation episode) was observed in a 20-yr integration of the general circulation model used for operational seasonal prediction by the U.S. National Weather Service. This motivated a detailed observational reassessment of the teleconnections between strong SST anomalies in the central equatorial Pacific Ocean and Pacific–North America region 700-hPa heights and U.S. surface temperatures and precipitation. The approach used consisted of formation of monthly mean composites formed separately from cases in which the SST anomaly in a key area of the central equatorial Pacific Ocean was either large and positive or large and negative. Extensive permutation tests were conducted to test null hypotheses of no signal in these composites. The results provided a substantial case for the presence of teleconnections to either the positive- or negative-SST anomalies in every month of the year. These signals were seasonally varying (sometimes with substantial month to month changes) and, when present for both SST-anomaly signs in a particular month, usually were not similarly phased patterns of opposite polarity (i.e., the SST–teleconnected variable relationships were most often nonlinear).

A suite of 13 45-yr integrations of the same model described above was run with global SST analyses reconstructed from the observational record. Corresponding composites from the model were formed and compared visually and quantitatively with the high-confidence observational signals. The quantitative comparisons included skill analyses utilizing a decomposition that relates the squared differences between two maps to phase correspondence and amplitude and bias error terms and analyses of the variance about composite means. For the latter, in the case of the model runs it was possible to estimate the portions of this variance attributable to case to case variation in SSTs and to internal variability. Comparisons to monthly mean maps and analyses of variance for the 20-yr run with SSTs fixed to January 1992 values were also made.

The visual and quantitative comparisons all revealed different aspects of prominent model systematic errors that have important implications for the optimum exploitation of the model for use in prediction. One of these implications was that the current model’s ensemble responses to SST forcing will not be optimally useful until after nonlinear correction of SST-field-dependent systematic errors.

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Zaizhong Ma, Lars Peter Riishøjgaard, Michiko Masutani, John S. Woollen, and George D. Emmitt

Abstract

The Global Wind Observing Sounder (GWOS) concept, which has been developed as a hypothetical space-based hybrid wind lidar system by NASA in response to the 2007 National Research Council (NRC) decadal survey, is expected to provide global wind profile observations with high vertical resolution, precision, and accuracy when realized. The assimilation of Doppler wind lidar (DWL) observations anticipated from the GWOS is being conducted as a series of observing system simulation experiments (OSSEs) at the Joint Center for Satellite Data Assimilation (JCSDA). A companion paper (Riishøjgaard et al.) describes the simulation of this lidar wind data and evaluates the impact on global numerical weather prediction (NWP) of the baseline GWOS using a four-telescope configuration to provide independent line-of-sight wind speeds, while this paper sets out to assess the NWP impact of GWOS equipped with alternative paired configurations of telescopes. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Global Forecast System (GFS) were used, at a resolution of T382 with 64 layers, as the assimilation system and the forecast model, respectively, in these lidar OSSEs. A set of 45-day assimilation and forecast experiments from 2 July to 15 August 2005 was set up and executed.

In this OSSE study, a control simulation utilizing all of the data types assimilated in the operational GSI/GFS system was compared to three OSSE simulations that added lidar wind data from the different configurations of telescopes (one-, two-, and four-look configurations). First, the root-mean-square error (RMSE) of wind analysis is compared against the nature run. A significant reduction of the stratospheric RMSE of wind analyses is found for all latitudes when lidar wind profiles are used in the assimilation system. The forecast impacts of lidar data on the wind and mass forecasts are also presented. In addition, the anomaly correlations (AC) of geopotential height forecasts at 500 hPa were evaluated to compare the control and different GWOS telescope configuration experiments. The results show that the assimilation of lidar data from the GWOS (one, two, or four looks) can improve the NCEP GFS wind and mass field forecasts. The addition of the simulated lidar wind observations leads to a statistically significant increase in AC scores.

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Sid-Ahmed Boukabara, Isaac Moradi, Robert Atlas, Sean P. F. Casey, Lidia Cucurull, Ross N. Hoffman, Kayo Ide, V. Krishna Kumar, Ruifang Li, Zhenglong Li, Michiko Masutani, Narges Shahroudi, Jack Woollen, and Yan Zhou

Abstract

A modular extensible framework for conducting observing system simulation experiments (OSSEs) has been developed with the goals of 1) supporting decision-makers with quantitative assessments of proposed observing systems investments, 2) supporting readiness for new sensors, 3) enhancing collaboration across the community by making the most up-to-date OSSE components accessible, and 4) advancing the theory and practical application of OSSEs. This first implementation, the Community Global OSSE Package (CGOP), is for short- to medium-range global numerical weather prediction applications. The CGOP is based on a new mesoscale global nature run produced by NASA using the 7-km cubed sphere version of the Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model and the January 2015 operational version of the NOAA global data assimilation (DA) system. CGOP includes procedures to simulate the full suite of observing systems used operationally in the global DA system, including conventional in situ, satellite-based radiance, and radio occultation observations. The methodology of adding a new proposed observation type is documented and illustrated with examples of current interest. The CGOP is designed to evolve, both to improve its realism and to keep pace with the advance of operational systems.

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Wayman E. Baker, Robert Atlas, Carla Cardinali, Amy Clement, George D. Emmitt, Bruce M. Gentry, R. Michael Hardesty, Erland Källén, Michael J. Kavaya, Rolf Langland, Zaizhong Ma, Michiko Masutani, Will McCarty, R. Bradley Pierce, Zhaoxia Pu, Lars Peter Riishojgaard, James Ryan, Sara Tucker, Martin Weissmann, and James G. Yoe

The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues.

Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)'s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone.

This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that will set the stage for space-based deployment. Forecast impact experiments with actual airborne DWL measurements collected over the North Atlantic in 2003 and assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model are a clear indication of the value of lidar-measured wind profiles. Airborne DWL measurements collected over the western Pacific in 2008 and assimilated into both the ECMWF and U.S. Navy operational models support the earlier findings.

These forecast impact experiments confirm observing system simulation experiments (OSSEs) conducted over the past 25–30 years. The addition of simulated DWL wind observations in recent OSSEs performed at the Joint Center for Satellite Data Assimilation (JCSDA) leads to a statistically significant increase in forecast skill.

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