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This paper describes the three-dimensional variational (3D-Var) data assimilation (DA) system for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS). Its core element is a multivariate background error covariance implemented through multiple linear variable changes, including a wind variable change from stream function and velocity potential to zonal- and meridional-wind components, a vertical linear regression representing wind–mass balance, and multiplication by a diagonal matrix of error standard deviations. The univariate spatial correlations for the "unbalanced" variables utilize the Background error on Unstructured Mesh Package (BUMP), which is one of the generic components in the JEDI framework. The variable changes and univariate correlations are modeled directly on the native MPAS unstructured mesh. BUMP provides utilities to diagnose parameters of the covariance model, such as correlation lengths, from an ensemble of forecast differences, though some manual adjustment of the parameters is necessary because of mismatches between the univariate correlation function assumed by BUMP and the correlation structure in the sample of forecast differences. The resulting multivariate covariances, as revealed by single-observation tests, are qualitatively similar to those found in previous global 3D-Var systems. Month-long cycling DA experiments using a global quasi-uniform 60 km mesh demonstrate that 3D-Var, as expected, performs somewhat worse than a pure ensemble-based covariance, while a hybrid covariance, which combines that used in 3D-Var with the ensemble covariance, significantly outperforms both 3D-Var and the pure ensemble covariance. Due to its simple workflow and minimal computational requirements, the JEDI-MPAS 3D-Var system can be useful for the research community. [ABSTRACT FROM AUTHOR]

An ensemble of 3D ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS). Basic software building blocks are reused from previously presented deterministic 3DEnVar functionality and combined with a formal experimental workflow manager in MPAS-Workflow. En-3DEnVar is used to produce an 80-member ensemble of analyses, which are cycled with ensemble forecasts in a 1-month experiment. The ensemble forecasts approximate a purely flow-dependent background error covariance (BEC) at each analysis time. The En-3DEnVar BECs and prior ensemble-mean forecast errors are compared to those produced by a similar experiment that uses the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF). The experiment using En-3DEnVar produces a similar ensemble spread to and slightly smaller errors than the EAKF. The ensemble forecasts initialized from En-3DEnVar and EAKF analyses are used as BECs in deterministic cycling 3DEnVar experiments, which are compared to a control experiment that uses 20-member MPAS-A forecasts initialized from Global Ensemble Forecast System (GEFS) initial conditions. The experimental ensembles achieve mostly equivalent or better performance than the off-the-shelf ensemble system in this deterministic cycling setting, although there are many obvious differences in configuration between GEFS and the two MPAS ensemble systems. An additional experiment that uses hybrid 3DEnVar, which combines the En-3DEnVar ensemble BEC with a climatological BEC, increases tropospheric forecast quality compared to the corresponding pure 3DEnVar experiment. The JEDI-MPAS En-3DEnVar is technically working and useful for future research studies. Tuning of observation errors and spread is needed to improve performance, and several algorithmic advancements are needed to improve computational efficiency for larger-scale applications. [ABSTRACT FROM AUTHOR]

On 24 September 2021, JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the Model Prediction Across Scales – Atmosphere (MPAS-A) built on the software framework of the Joint Effort for Data assimilation Integration (JEDI) was publicly released for community use. Operating directly on the native MPAS unstructured mesh, JEDI-MPAS capabilities include three-dimensional variational (3DVar) and ensemble–variational (EnVar) schemes as well as the ensemble of DA (EDA) technique. On the observation side, one advanced feature in JEDI-MPAS is the full all-sky approach for satellite radiance DA with the introduction of hydrometeor analysis variables. This paper describes the formulation and implementation of EnVar for JEDI-MPAS. JEDI-MPAS 1.0.0 is evaluated with month-long cycling 3DEnVar experiments with a global 30–60 km dual-resolution configuration. The robustness and credible performance of JEDI-MPAS are demonstrated by establishing a benchmark non-radiance DA experiment, then incrementally adding microwave radiances from three sources: Advanced Microwave Sounding Unit-A (AMSU-A) temperature sounding channels in clear-sky scenes, AMSU-A window channels in all-sky scenes, and Microwave Humidity Sounder (MHS) water vapor channels in clear-sky scenes. JEDI-MPAS 3DEnVar behaves well with a substantial and significant positive impact obtained for almost all aspects of forecast verification when progressively adding more microwave radiance data. In particular, the day 5 forecast of the best-performing JEDI-MPAS experiment yields an anomaly correlation coefficient (ACC) of 0.8 for 500 hPa geopotential height, a gap of roughly a half day when compared to cold-start forecasts initialized from operational analyses of the National Centers for Environmental Prediction, whose ACC does not drop to 0.8 until a lead time of 5.5 d. This indicates JEDI-MPAS's great potential for both research and operations. [ABSTRACT FROM AUTHOR]

An increasing number of observations have contributed to the performance of numerical weather prediction systems. Accordingly, it is important to evaluate the impact of these observations on forecast accuracy. While the observing system experiment (OSE) requires considerable computational resources, the adjoint-derived method can evaluate the impact of all observational components at a lower cost. In this study, the effect of observations on forecasts is evaluated by the adjoint-derived method using the Weather Research and Forecasting Model, its adjoint model, and a corresponding three-dimensional variational data assimilation system in East Asia and the western North Pacific for the 2008 typhoon season. Radiance observations had the greatest total impact on forecasts, but conventional wind observations had the greatest impact per observation. For each observation type, the total impact was greatest for radiosonde and each Advanced Microwave Sounding Unit (AMSU)-A satellite, followed by surface synoptic observation from a land station (SYNOP), Quick Scatterometer (QuikSCAT), atmospheric motion vector (AMV) wind from a geostationary satellite (GEOAMV), and aviation routine weather reports (METARs). The fraction of beneficial observations was approximately 60%-70%, which is higher than that reported in previous studies. For several analyses of Typhoons Sinlaku (200813) and Jangmi (200815), dropsonde soundings taken near the typhoon had similar or greater observation impacts than routine radiosonde soundings. The sensitivity to the error covariance parameter indicates that reducing (increasing) observation (background) error covariance helps to reduce forecast error in the current analysis framework. The observation impact from OSEs is qualitatively similar to that from the adjoint method for major observation types. This study confirms that radiosonde observations provide primary information on the atmospheric state as in situ observations and that satellite radiances are an essential component of atmospheric observation systems. [ABSTRACT FROM AUTHOR]

During August and September 2008, The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) was conducted to investigate the formation, structure, targeted observation, extratropical transition (ET) and downstream effects of tropical cyclones (TCs) in the Western North Pacific (WNP) region. This study investigates the effect of targeted dropsonde observations from T-PARC and the TC best track data on the track forecast of Typhoon Sinlaku (2008). A WRF-based ensemble Kalman filter (EnKF) is used for a series of observation system experiments (OSEs). From the innovation statistics and rank histograms, the EnKF behaves well in terms of ensemble spread, despite some spread deficiency in low-tropospheric winds and warm and moist biases. Assimilation of targeted dropsonde observations leads to improved initial position and subsequent track forecast compared with experiments that only assimilate conventional observations. In the meantime, assimilation of TC position reduces the initial position error, whereas assimilation of minimum sea level pressure (SLP) information is efficient to analyse the strong vortex structures of TC and reduces track forecast errors. Assimilation of TC position and minimum SLP information is particularly beneficial when dropsonde observations do not exist. [ABSTRACT FROM AUTHOR]

In this study, structures of real-time adaptive observation guidance provided by Yonsei University (YSU) in South Korea during The Observing System Research and Predictability Experiment (THORPEX)-Pacific Asian Regional Campaign (T-PARC) are presented and compared with those of no-lead-time adaptive observation guidance recalculated as well as other adaptive observation guidance for a tropical cyclone (Jangmi 200815). During the T-PARC period, real-time dry total energy (TE) singular vectors (SVs) based on the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) and the corresponding tangent linear and adjoint models with a Lanczos algorithm are provided by YSU to help determine sensitive regions for targeted observations. While YSU provided the real-time TESV guidance based on a mesoscale model, other institutes provided real-time TESV guidance based on global models. The overall features of the real-time MM5 TESVs were similar to those generated from global models, showing influences from tropical cyclones, midlatitude troughs, and subtropical ridges. TESV structures are very sensitive to verification region and forecast lead time. If a more accurate basic-state trajectory with no lead time is used, more accurate TESVs, which yield more accurate determinations of sensitive regions for targeted observations, may be calculated. The results of this study may imply that reducing forecast lead time is an important component to obtaining better sensitivity guidance for real-time targeted observation operations. [ABSTRACT FROM AUTHOR]

In this study, the structure and evolution of total energy singular vectors (SVs) of Typhoon Usagi (2007) are evaluated using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) and its tangent linear and adjoint models with a Lanczos algorithm. Horizontal structures of the initial SVs following the tropical cyclone (TC) evolution suggest that, relatively far from the region of TC recurvature, SVs near the TC center have larger magnitudes than those in the midlatitude trough. The SVs in the midlatitude trough region become dominant as the TC passes by the region of recurvature. Increasing magnitude of the SVs over the midlatitude trough regions is associated with the extratropical transition of the TC. While the SV sensitivities near the TC center are mostly associated with warming in themidtroposphere and inflow toward the TC along the edge of the subtropical high, the SVsensitivities in themidlatitude are located under the upper trough with upshear-tilted structures and associated with strong baroclinicity and frontogenesis in the lower troposphere. Given the results in this study, sensitive regions for adaptive observations of TCs may be different following the TC development stage. Far from the TC recurvature, sensitive regions near TC center may be important. Closer to the TC recurvature, effects of the midlatitude trough become dominant and the vertical structures of the SVs in the midlatitude are basically similar to those of extratropical cyclones. [ABSTRACT FROM AUTHOR]

In this study, the structures and growth rates of singular vectors (SVs) for Typhoon Usagi were investigated using different moist physics and norms. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and its tangent linear and adjoint models with a Lanczos algorithm were used to calculate SVs over a 36-h period. The moist physics used for linear (i.e., tangent linear and adjoint model) integrations is large-scale precipitation, and the norms used are dry and moist total energy (TE) norms. Overall, moist physics in linear integrations and a moist TE norm increase the growth rates of SVs and cause smaller horizontal structures and vertical distributions closer to the lower boundary. With a dry TE norm, the SV energy distributions show similar (dissimilar) large- (small-) scale horizontal SV structures for experiments, regardless of physics. The SVs with moist linear physics and a moist TE norm have maximum horizontal energy structures near the typhoon center. With a small weighting on the moisture term in the moist TE norm, both the remote and nearby influences on the TC are indicated by the horizontal SV energy distributions. The kinetic energy shows the largest contributions to the vertical SV TE distributions in most of the experiments, except for the largest moisture (potential energy) contributions to the SV TE at the final (initial) time in the moist TE norm (dry and weighted moist TE norms at uppermost levels). In contrast, the SV vorticity distributions show more consistent structures among experiments with different linear physics and norms, implying that, in terms of the rotational component of the wind field, the SVs are not sensitive to the choice of moist physics and norms. Given large-scale precipitation as the linear moist physics, the SV energy structures and growth rate with a moist TE norm show the largest difference when compared with those with other norms. [ABSTRACT FROM AUTHOR]