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Lili Lei
,
Jeffrey L. Anderson
, and
Glen S. Romine

Abstract

For ensemble-based data assimilation, localization is used to limit the impact of observations on physically distant state variables to reduce spurious error correlations caused by limited ensemble size. Traditionally, the localization value applied is spatially homogeneous. Yet there are potentially larger errors and different covariance length scales in precipitation systems, and that may justify the use of different localization functions for precipitating and nonprecipitating regions. Here this is examined using empirical localization functions (ELFs). Using output from an ensemble observing system simulation experiment (OSSE), ELFs provide estimates of horizontal and vertical localization for different observation types in regions with and without precipitation. For temperature and u- and υ-wind observations, the ELFs for precipitating regions are shown to have smaller horizontal localization scales than for nonprecipitating regions. However, the ELFs for precipitating regions generally have larger vertical localization scales than for nonprecipitating regions. The ELFs are smoothed and then applied in three additional OSSEs. Spatially homogeneous ELFs are found to improve performance relative to a commonly used localization function with compact support. When different ELFs are applied in precipitating and nonprecipitating regions, performance is further improved, but varying ELFs by observation type was not found to be as important. Imbalance in initial states caused by use of different localization functions is diagnosed by the domain-averaged surface pressure tendency. Forecasts from analyses with ELFs have smaller surface pressure tendencies than the standard localization, indicating improved initial balance with ELFs.

Full access
Sijing Ren
,
Lili Lei
,
Zhe-Min Tan
, and
Yi Zhang

Abstract

Ensemble sensitivity is often a diagonal approximation to the multivariate regression, leading to a simple univariate regression. Comparatively, the multivariate ensemble sensitivity retains the full covariance matrix when computing the multivariate regression. The performances of both univariate and multivariate ensemble sensitivities in multiscale flows have not been thoroughly examined, and the demonstration of the latter in realistic applications has been sparse. A high-resolution ensemble forecast of Typhoon Haiyan (2013) is used to examine the performances of the two ensemble sensitivities. Compared to the multivariate sensitivity, the univariate sensitivity overestimates the forecast metric, especially at higher levels. To increase the predicted Haiyan’s intensity, multivariate ensemble sensitivity gives initial perturbations characterized by a warming area around the center of the storm, an increased moisture area around the eyewall, a stronger primary circulation around the radius of maximum wind, and stronger inflow at low levels and stronger outflow at high levels. Perturbed initial condition experiments verify that the predicted response from the multivariate sensitivity is more accurate than that from the univariate sensitivity. Therefore, the ability of multivariate sensitivity to provide more accurate predicted responses than the univariate sensitivity has been demonstrated in a realistic multiscale flow application.

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Yueya Wang
,
Xiaoming Shi
,
Lili Lei
, and
Jimmy Chi-Hung Fung

Abstract

Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete streamfunction observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from ensemble square root filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations.

Significance Statement

Data assimilation plays a critical role in improving the skills of modern numerical weather prediction by establishing accurate initial conditions. However, unobservable regions are common in observation data, particularly those derived from remote sensing. The nonlinear relationship between data from observable regions and the physical state of unobservable regions may impede DA efficiency. As a result, we propose that deep learning be used to improve data assimilation in such cases by reconstructing a spatially complete first guess of the physical state with deep learning and then applying data assimilation to the reconstructed field. Such deep learning augmentation is found effective in improving the accuracy of data assimilation, especially for sparse observation and small ensemble size.

Full access
Linfan Zhou
,
Lili Lei
,
Zhe-Min Tan
,
Yi Zhang
, and
Di Di

Abstract

All-sky radiance assimilation often has non-Gaussian observation error distributions, which can be exacerbated by high model spatial resolutions due to better resolved nonlinear physical processes. For ensemble Kalman filters, observation ensemble perturbations can be approximated by the linearized observation operator (LinHx) that uses the observation operator Jacobian of ensemble mean rather than the full observation operator (FullHx). The impact of observation operator on infrared radiance data assimilation is examined here by assimilating synthetic radiance observations from channel 1025 of GIIRS with increased model spatial resolutions from 7.5 km to 300 m. A tropical cyclone is used, while the findings are expected to be generally applied. Compared to FullHx, LinHx provides larger magnitudes of correlations and stronger corrections around observation locations, especially when all-sky radiances are assimilated at fine model resolutions. For assimilating clear-sky radiances with increasing model resolutions, LinHx has smaller errors and improved vortex intensity and structure than FullHx. But when all-sky radiances are assimilated, FullHx has advantages over LinHx. Thus, for regimes with more linearity, LinHx provides stronger correlations and imposes more corrections than FullHx; but for regimes with more nonlinearity, LinHx provides detrimental non-Gaussian prior error distributions in observation space, unrealistic correlations, and overestimated corrections, compared to FullHx.

Significance Statement

Assimilating satellite radiances has been essential for numerical weather prediction. All-sky radiance assimilation can improve the analyses and forecasts of tropical cyclones, but it often has non-Gaussian observation error distributions. With increased model resolutions, nonlinear physical processes can be better resolved, which leads to non-Gaussian error distributions of state variables. Nonlinearity and non-Gaussianity impose great challenges for data assimilation (DA), since most DA theories assume linear processes and Gaussian error distributions. As the spatial resolutions of observations and numerical models will keep increasing, the potential issues of assimilating high-spatial-resolution observations given fine model resolutions need to be examined. The linearized observation forward operator, as an alternative to remedy non-Gaussianity for radiance DA, is investigated. With model resolution increasing from 7.5 km to 300 m, the linearized observation operator has advantages over the full observation operator for assimilating clear-sky radiances, but the opposite is true for assimilating all-sky radiances.

Free access
Linfan Zhou
,
Lili Lei
,
Jeffrey S. Whitaker
, and
Zhe-Min Tan

Abstract

Hyperspectral infrared (IR) satellites can provide high-resolution vertical profiles of the atmospheric state, which significantly contributes to the forecast skill of numerical weather prediction, especially for regions with sparse observations. One challenge in assimilating the hyperspectral radiances is how to effectively extract the observation information, due to the interchannel correlations and correlated observation errors. An adaptive channel selection method is proposed, which is implemented within the data assimilation scheme and selects the radiance observation with the maximum reduction of variance in observation space. Compared to the commonly used channel selection method based on the maximum entropy reduction (ER), the adaptive method can provide flow-dependent and time-varying channel selections. The performance of the adaptive selection method is evaluated by assimilating only the synthetic Fengyun-4A (FY-4A) GIIRS IR radiances in an observing system simulation experiment (OSSE), with model resolutions from 7.5 to 1.5 km and then 300 m. For both clear-sky and all-sky conditions, the adaptive method generally produces smaller RMS errors of state variables than the ER-based method given similar amounts of assimilated radiances, especially with fine model resolutions. Moreover, the adaptive method has minimum RMS errors smaller than or approaching those with all channels assimilated. For the intensity of the tropical cyclone, the adaptive method also produces smaller errors of the minimum dry air mass and maximal wind speed at different levels, compared to the ER-based selection method.

Significance Statement

Assimilating satellite radiances has been essential for numerical weather prediction. Hyperspectral infrared satellites provide high-resolution vertical profiles for the atmospheric state and can further improve the numerical weather prediction. Due to limited computational resources, and correlated observations and associated errors, efficient and effective ways to assimilate the hyperspectral radiances are needed. An adaptive channel selection method that is incorporated with data assimilation is proposed. The adaptive channel selection can effectively extract the information from hyperspectral radiances under both clear- and all-sky conditions, with increased model resolutions from kilometers to subkilometers.

Restricted access
Nedjeljka Žagar
,
Jeffrey Anderson
,
Nancy Collins
,
Timothy Hoar
,
Kevin Raeder
,
Lili Lei
, and
Joseph Tribbia

Abstract

Global data assimilation systems for numerical weather prediction (NWP) are characterized by significant uncertainties in tropical analysis fields. Furthermore, the largest spread of global ensemble forecasts in the short range on all scales is in the tropics. The presented results suggest that these properties hold even in the perfect-model framework and the ensemble Kalman filter data assimilation with a globally homogeneous network of wind and temperature profiles. The reasons for this are discussed by using the modal analysis, which provides information about the scale dependency of analysis and forecast uncertainties and information about the efficiency of data assimilation to reduce the prior uncertainties in the balanced and inertio-gravity dynamics.

The scale-dependent representation of variance reduction of the prior ensemble by the data assimilation shows that the peak efficiency of data assimilation is on the synoptic scales in the midlatitudes that are associated with quasigeostrophic dynamics. In contrast, the variance associated with the inertia–gravity modes is less successfully reduced on all scales. A smaller information content of observations on planetary scales with respect to the synoptic scales is discussed in relation to the large-scale tropical uncertainties that current data assimilation methodologies do not address successfully. In addition, it is shown that a smaller reduction of the large-scale uncertainties in the prior state for NWP in the tropics than in the midlatitudes is influenced by the applied radius for the covariance localization.

Full access
Lei Yang
,
Dongxiao Wang
,
Jian Huang
,
Xin Wang
,
Lili Zeng
,
Rui Shi
,
Yunkai He
,
Qiang Xie
,
Shengan Wang
,
Rongyu Chen
,
Jinnan Yuan
,
Qiang Wang
,
Ju Chen
,
Tingting Zu
,
Jian Li
,
Dandan Sui
, and
Shiqiu Peng

Abstract

Air–sea interaction in the South China Sea (SCS) has direct impacts on the weather and climate of its surrounding areas at various spatiotemporal scales. In situ observation plays a vital role in exploring the dynamic characteristics of the regional circulation and air–sea interaction. Remote sensing and regional modeling are expected to provide high-resolution data for studies of air–sea coupling; however, careful validation and calibration using in situ observations is necessary to ensure the quality of these data. Through a decade of effort, a marine observation network in the SCS has begun to be established, yielding a regional observatory for the air–sea synoptic system.

Earlier observations in the SCS were scarce and narrowly focused. Since 2004, an annual series of scientific open cruises during late summer in the SCS has been organized by the South China Sea Institute of Oceanology (SCSIO), carefully designed based on the dynamic characteristics of the oceanic circulation and air–sea interaction in the SCS region. Since 2006, the cruise carried a radiometer and radiosondes on board, marking a new era of marine meteorological observation in the SCS. Fixed stations have been established for long-term and sustained records. Observations obtained through the network have been used to study regional ocean circulation and processes in the marine atmospheric boundary layer. In the future, a great number of multi-institutional, collaborative scientific cruises and observations at fixed stations will be carried out to establish a mesoscale hydrological and marine meteorological observation network in the SCS.

Full access
Arianna Valmassoi
,
Jan D. Keller
,
Daryl T. Kleist
,
Stephen English
,
Bodo Ahrens
,
Ivan Bašták Ďurán
,
Elisabeth Bauernschubert
,
Michael G. Bosilovich
,
Masatomo Fujiwara
,
Hans Hersbach
,
Lili Lei
,
Ulrich Löhnert
,
Nabir Mamnun
,
Cory R. Martin
,
Andrew Moore
,
Deborah Niermann
,
Juan José Ruiz
, and
Leonhard Scheck
Open access