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Nancy L. Baker

Abstract

A new, operational meteorological database has been developed to provide quality-controlled observations for the atmospheric analysis and prediction systems at Fleet Numerical Oceanography Center (FNOC). The quality control procedures for the database were developed by Atmospheric Directorate of the Naval Oceanographic and Atmospheric Research Laboratory (now the Naval Research Laboratory), which is collocated with FNOC in Monterey, California. Global observations of atmospheric variables are subjected to various quality control techniques before being added to this database. During preliminary data processing, all observations are checked gross-error limits, multilevel reports are checked for vertical consistency, and multivariable reports are checked for internal consistency. Subsequent quality tests are performed in the analysis to ensure consistency between the observed data and the analysis background and to verify that the observations are supported by the surrounding reports. Additionally, several options are available for subjective intervention, including the creation of synthetic marine observations or the deletion of ship reports. Finally, efforts are being made to use archived quality statistics to determine bias corrections and quality estimates for radiosonde stations.

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Patricia M. Pauley, Nancy L. Baker, and Edward H. Barker

On 29 November 1991 a series of collisions involving 164 vehicles occurred on Interstate 5 in the San Joaquin Valley in California in a dust storm that reduced the visibility to near zero. The accompanying high surface winds are hypothesized to result from intense upper-tropospheric downward motion that led to the formation of a strong upper front and tropopause fold and that transported high momentum air downward to midlevels where boundary layer processes could then mix it to the surface. The objectives of the research presented in this paper are to document the event, to provide support for the hypothesis that both upper-level and boundary layer processes were important, and to determine the structure of the mesoscale circulations in this case for future use in evaluating the navy's mesoscale data assimilation system.

The strong upper-level descent present in this case is consistent with what one would expect for jet streak and frontal circulations in combination with quasigeostrophic processes. During the period examined, upper-level data and analyses portray a strong upper-tropospheric jet streak with maximum winds initially in excess of 85 m s−1 (≈170 kt) that weakened as it propagated southward around the base of a long-wave trough. The jet streak was accompanied by a strong upper front and tropopause fold, both of which imply intense downward motion. The vertical motion field near the time of the accidents had two maxima—one that was associated with a combination of quasigeostrophic forcing and terrain-induced descent in the lee of the Sierra and one that was associated with the descending branch of the secondary circulation in the jet streak exit region and the cold advection by both the geostrophic wind and the ageostrophic wind in the upper front. The 700-hPa wind speed maximum over and west of the San Joaquin Valley overlapped with the latter maximum, supporting the hypothesized role of downward momentum transport.

Given the significant 700-hPa wind speeds over the San Joaquin Valley during daytime hours on the day of the collisions, boundary layer mixing associated with solar heating of the earth's surface was then able to generate high surface winds. Once the high surface winds began, a dust storm was inevitable, since winter rains had not yet started and soil conditions were drier than usual in this sixth consecutive drought year. Surface observations from a variety of sources depict blowing dust and high surface winds at numerous locations in the San Joaquin Valley, the Mojave and other desert sites, and in the Los Angeles Basin and other south coast sites. High surface winds and low visibilities began in the late morning at desert and valley sites and lasted until just after sunset, consistent with the hypothesized heating-induced mixing. The 0000 UTC soundings in California portrayed an adiabatic layer from the surface to at least 750 hPa, also supporting the existence of mixing. On the other hand, the high winds in the Los Angeles Basin began near sunset in the wake of a propagating mesoscale trough that appeared to have formed in the lee of the mountains that separate the Los Angeles Basin from the San Joaquin Valley.

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Heather Purdie, Nancy Bertler, Andrew Mackintosh, Joel Baker, and Rachael Rhodes

Abstract

The authors present stable water isotope and trace element data for fresh winter snow from two temperate maritime glaciers located on opposite sides of the New Zealand Southern Alps. The isotopes δ 18O and δD were more depleted at the eastern Tasman Glacier site because of prevailing westerly flow and preferential rainout of heavy isotopes as air masses crossed the Alps. The deuterium excess provided some indication of moisture provenance, with the Tasman Sea contributing ∼70% of the moisture received at Franz Josef and Tasman Glaciers. This source signal was also evident in trace elements, with a stronger marine signal (Na, Mg, and Sr) associated with snow from the Tasman Sea and larger concentrations of terrestrial species (Pb, V, and Zr) in air masses from the Southern and Pacific Oceans. Although postdepositional modification of signals was detected, the results indicate that there is exciting potential to learn more about climate trends and moisture source pathways and to learn from geochemical signals contained in snow and ice in the New Zealand region.

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David D. Kuhl, Thomas E. Rosmond, Craig H. Bishop, Justin McLay, and Nancy L. Baker

Abstract

The effect on weather forecast performance of incorporating ensemble covariances into the initial covariance model of the four-dimensional variational data assimilation (4D-Var) Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) is investigated. This NAVDAS-AR-hybrid scheme linearly combines the static NAVDAS-AR initial background error covariance with a covariance derived from an 80-member flow-dependent ensemble. The ensemble members are generated using the ensemble transform technique with a (three-dimensional variational data assimilation) 3D-Var-based estimate of analysis error variance. The ensemble covariances are localized using an efficient algorithm enabled via a separable formulation of the localization matrix. The authors describe the development and testing of this scheme, which allows for assimilation experiments using differing linear combinations of the static and flow-dependent background error covariances. The tests are performed for two months of summer and two months of winter using operational model resolution and the operational observational dataset, which is dominated by satellite observations. Results show that the hybrid mode data assimilation scheme significantly reduces the forecast error across a wide range of variables and regions. The improvements were particularly pronounced for tropical winds. The verification against radiosondes showed a greater than 0.5% reduction in vector wind RMS differences in areas of statistical significance. The verification against self-analysis showed a greater than 1% reduction from verifying against analyses between 2- and 5-day lead time at all eight vertical levels examined in areas of statistical significance. Using the Navy's summary of verification results, the Navy Operational Global Atmospheric Prediction System (NOGAPS) scorecard, the improvements resulted in a score (+1) that justifies a major system upgrade.

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Xuguang Wang, Hristo G. Chipilski, Craig H. Bishop, Elizabeth Satterfield, Nancy Baker, and Jeffrey S. Whitaker

Abstract

A new multiscale, ensemble-based data assimilation (DA) method, multiscale local gain form ensemble transform Kalman filter (MLGETKF), is introduced. MLGETKF allows simultaneous update of multiple scales for both the ensemble mean and perturbations through assimilating all observations at once. MLGETKF performs DA in independent local volumes, which lends the algorithm a high degree of computational scalability. The multiscale analysis is enabled through the rapid creation of many pseudoensemble perturbations via a multiscale ensemble modulation procedure. The Kalman gain that is used to update the raw background ensemble mean and perturbations is based on this modulated ensemble, which intrinsically includes multiscale model space localization. Experiments with a noncycled statistical model show that the full background covariance estimated by MLGETKF more accurately resembles the shape of the true covariance than a scale-unaware localization. The mean analysis from the best-performing MLGETKF is statistically significantly more accurate than the best-performing scale-unaware LGETKF. The accuracy of the MLGETKF analysis is more sensitive to small-scale band localization radius than large-scale band. MLGETKF is further examined in a cycling DA context with a surface quasigeostrophic model. The root-mean-square potential temperature analysis error of the best-performing MLGETKF is 17.2% lower than that of the best-performing LGETKF. MLGETKF reduces analysis errors measured in kinetic energy spectra space by 30%–80% relative to LGETKF with the largest improvement at large scales. MLGETKF deterministic and ensemble mean forecasts are more accurate than LGETKF for full and large scales up to 5–6-day lead time and for small scales up to 3–4-day lead time, gaining ~12 h–1 day of predictability.

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William F. Campbell, Elizabeth A. Satterfield, Benjamin Ruston, and Nancy L. Baker

Abstract

Appropriate specification of the error statistics for both observational data and short-term forecasts is necessary to produce an optimal analysis. Observation error stems from instrument error, forward model error, and error of representation. All sources of observation error, particularly error of representation, can lead to nonzero correlations. While correlated forecast error has been accounted for since the early days of atmospheric data assimilation, observation error has typically been treated as uncorrelated until relatively recently. Thinning, averaging, and/or inflation of the assigned observation error variance have been employed to compensate for unaccounted error correlations, especially for high-resolution satellite data.

In this study, the benefits of accounting for nonzero vertical (interchannel) correlation for both the Advanced Technology Microwave Satellite (ATMS) and Infrared Atmospheric Sounding Interferometer (IASI) in the NRL Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) are assessed. The vertical observation error covariance matrix for the ATMS and IASI instruments was estimated using the Desroziers method. The results suggest lowering the assigned error variance and introducing strong correlations, especially in the moisture-sensitive channels. Strong positive impact on forecast skill (verified against both the ECMWF analyses and high-quality radiosonde data) is shown in both the ATMS and IASI instruments. Additionally, the convergence of the iterative solver in NAVDAS-AR can be improved by small modifications to the observation error covariance matrices, resulting in further reduction in RMS error.

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Karl W. Hoppel, Stephen D. Eckermann, Lawrence Coy, Gerald E. Nedoluha, Douglas R. Allen, Steven D. Swadley, and Nancy L. Baker

Abstract

Upper atmosphere sounding (UAS) channels of the Special Sensor Microwave Imager/Sounder (SSMIS) were assimilated using a high-altitude version of the Navy Global Environmental Model (NAVGEM) in order to investigate their potential for operational forecasting from the surface to the mesospause. UAS radiances were assimilated into NAVGEM using the new Community Radiative Transfer Model (CRTM) that accounts for Zeeman line splitting by geomagnetic fields. UAS radiance data from April 2010 to March 2011 are shown to be in good agreement with coincident temperature measurements from the Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) instrument that were used to simulate UAS brightness temperatures. Four NAVGEM experiments were performed during July 2010 that assimilated (i) no mesospheric observations, (ii) UAS data only, (iii) SABER and Microwave Limb Sounder (MLS) mesospheric temperatures only, and (iv) SABER, MLS, and UAS data. Zonal mean temperatures and observation − forecast differences for the UAS-only and SABER+MLS experiments are similar throughout most of the mesosphere, and show large improvements over the experiment assimilating no mesospheric observations, proving that assimilation of UAS radiances can provide a reliable large-scale constraint throughout the mesosphere for operational, high-altitude analysis. This is confirmed by comparison of solar migrating tides and the quasi-two-day wave in the mesospheric analyses. The UAS-only experiment produces realistic tidal and two-day wave amplitudes in the summer mesosphere in agreement with the experiments assimilating MLS and SABER observations, whereas the experiment with no mesospheric observations produces excessively strong mesospheric winds and two-day wave amplitudes.

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Rebecca E. Stone, Carolyn A. Reynolds, James D. Doyle, Rolf H. Langland, Nancy L. Baker, David A. Lavers, and F. Martin Ralph

Abstract

Atmospheric rivers, often associated with impactful weather along the west coast of North America, can be a challenge to forecast even on short time scales. This is attributed, at least in part, to the scarcity of eastern Pacific in situ observations. We examine the impact of assimilating dropsonde observations collected during the Atmospheric River (AR) Reconnaissance 2018 field program on the Navy Global Environmental Model (NAVGEM) analyses and forecasts. We compare NAVGEM’s representation of the ARs to the observations, and examine whether the observation–background difference statistics are similar to the observation error variance specified in the data assimilation system. Forecast sensitivity observation impact is determined for each dropsonde variable, and compared to the impacts of the North American radiosonde network. We find that the reconnaissance soundings have significant beneficial impact, with per observation impact more than double that of the North American radiosonde network. Temperature and wind observations have larger total and per observation impact than moisture observations. In our experiment, the 24-h global forecast error reduction from the reconnaissance soundings can be comparable to the reduction from the North American radiosonde network for the field program dates that include at least two flights.

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Stephen D. Eckermann, Jun Ma, Karl W. Hoppel, David D. Kuhl, Douglas R. Allen, James A. Doyle, Kevin C. Viner, Benjamin C. Ruston, Nancy L. Baker, Steven D. Swadley, Timothy R. Whitcomb, Carolyn A. Reynolds, Liang Xu, N. Kaifler, B. Kaifler, Iain M. Reid, Damian J. Murphy, and Peter T. Love

Abstract

A data assimilation system (DAS) is described for global atmospheric reanalysis from 0- to 100-km altitude. We apply it to the 2014 austral winter of the Deep Propagating Gravity Wave Experiment (DEEPWAVE), an international field campaign focused on gravity wave dynamics from 0 to 100 km, where an absence of reanalysis above 60 km inhibits research. Four experiments were performed from April to September 2014 and assessed for reanalysis skill above 50 km. A four-dimensional variational (4DVAR) run specified initial background error covariances statically. A hybrid-4DVAR (HYBRID) run formed background error covariances from an 80-member forecast ensemble blended with a static estimate. Each configuration was run at low and high horizontal resolution. In addition to operational observations below 50 km, each experiment assimilated 105 observations of the mesosphere and lower thermosphere (MLT) every 6 h. While all MLT reanalyses show skill relative to independent wind and temperature measurements, HYBRID outperforms 4DVAR. MLT fields at 1-h resolution (6-h analysis and 1–5-h forecasts) outperform 6-h analysis alone due to a migrating semidiurnal (SW2) tide that dominates MLT dynamics and is temporally aliased in 6-h time series. MLT reanalyses reproduce observed SW2 winds and temperatures, including phase structures and 10–15-day amplitude vacillations. The 0–100-km reanalyses reveal quasi-stationary planetary waves splitting the stratopause jet in July over New Zealand, decaying from 50 to 80 km then reintensifying above 80 km, most likely via MLT forcing due to zonal asymmetries in stratospheric gravity wave filtering.

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