The NCEP and NCAR are cooperating in a project (denoted "reanalysis") to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957-96. This eliminates perceived climate jumps associated with changes in the data assimilation system.The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided by different countries and organizations. The system has been designed with advanced quality control and monitoring components, and can produce 1 mon of reanalysis per day on a Cray YMP/8 supercomputer. Different types of output archives are being created to satisfy different user needs, including a "quick look" CD-ROM (one per year) with six tropospheric and stratospheric fields available twice daily, as well as surface, top-of-the-atmosphere, and isentropic fields. Reanalysis information and selected output is also available on-line via the Internet (http//:nic.fb4.noaa.gov:8000). A special CD-ROM, containing 13 years of selected observed, daily, monthly, and climatological data from the NCEP/NCAR Reanalysis, is included with this issue. Output variables are classified into four classes, depending on the degree to which they are influenced by the observations and/or the model. For example, "C" variables (such as precipitation and surface fluxes) are completely determined by the model during the data assimilation and should be used with caution. Nevertheless, a comparison of these variables with observations and with several climatologies shows that they generally contain considerable useful information. Eight-day forecasts, produced every 5 days, should be useful for predictability studies and for monitoring the quality of the observing systems.The 40 years of reanalysis should be completed in early 1997. A continuation into the future through an identical Climate Data Assimilation System will allow researchers to reliably compare recent anomalies with those in earlier decades. Since changes in the observing systems will inevitably produce perceived changes in the climate, parallel reanalyses (at least 1 year long) will be generated for the periods immediately after the introduction of new observing systems, such as new types of satellite data.NCEP plans currently call for an updated reanalysis using a state-of-the-art system every five years or so. The successive reanalyses will be greatly facilitated by the generation ...

This article reviews developments towards assimilating cloud‐ and precipitation‐ affected satellite radiances at operational forecasting centres. Satellite data assimilation is moving beyond the “clear‐sky” approach that discards any observations affected by cloud. Some centres already assimilate cloud‐ and precipitation‐affected radiances operationally and the most popular approach is known as “all‐sky,” which assimilates all observations directly as radiances, whether they are clear, cloudy or precipitating, using models (for both radiative transfer and forecasting) that are capable of simulating cloud and precipitation with sufficient accuracy. Other frameworks are being tried, including the assimilation of humidity retrieved from cloudy observations using Bayesian techniques. Although the all‐sky technique is now proven for assimilation of microwave radiances, it has yet to be demonstrated operationally for infrared radiances, though several centres are getting close. Assimilating frequently available all‐sky infrared observations from geostationary satellites could give particular benefit for short‐range forecasting. More generally, assimilating cloud‐ and precipitation‐affected satellite observations improves forecasts in the medium range globally and can also improve the analysis and shorter‐range forecasting of otherwise poorly observed weather phenomena as diverse as tropical cyclones and wintertime low cloud.

In this work, Janus monolayers are predicted for a new 2D MA2Z4 family by means of first-principles calculations. The predicted MSiGeN4 (M = Mo and W) monolayers exhibit dynamic, thermodynamic and mechanical stability, and they are indirect band-gap semiconductors.

The capability of all-sky microwave radiance assimilation in the Gridpoint Statistical Interpolation (GSI) analysis system has been developed at the National Centers for Environmental Prediction (NCEP). This development effort required the adaptation of quality control, observation error assignment, bias correction, and background error covariance to all-sky conditions within the ensemble–variational (EnVar) framework. The assimilation of cloudy radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) microwave radiometer for ocean fields of view (FOVs) is the primary emphasis of this study. In the original operational hybrid 3D EnVar Global Forecast System (GFS), the clear-sky approach for radiance data assimilation is applied. Changes to data thinning and quality control have allowed all-sky satellite radiances to be assimilated in the GSI. Along with the symmetric observation error assignment, additional situation-dependent observation error inflation is employed for all-sky conditions. Moreover, in addition to the current radiance bias correction, a new bias correction strategy has been applied to all-sky radiances. In this work, the static background error variance and the ensemble spread of cloud water are examined, and the levels of cloud variability from the ensemble forecast in single- and dual-resolution configurations are discussed. Overall, the all-sky approach provides more realistic simulated brightness temperatures and cloud water analysis increments, and improves analysis off the west coasts of the continents by reducing a known bias in stratus. An approximate 10% increase in the use of AMSU-A channels 1–5 and a 12% increase for channel 15 are also observed. The all-sky AMSU-A radiance assimilation became operational in the 4D EnVar GFS system upgrade of 12 May 2016.

The adjoint of a data assimilation system provides an efficient way of estimating sensitivities of analysis or forecast measures with respect to observations. The NASA Global Modeling and Assimilation Office (GMAO) has developed an exact adjoint of the Gridpoint Statistical Interpolation (GSI) analysis scheme developed at the National Centers for Environmental Prediction (NCEP). The development approach is unique in that the adjoint is derived from a line-by-line tangent linear version of the GSI. Availability of the tangent linear scheme provides an explicit means of assessing not only the fidelity of the adjoint, but also the effects of nonlinear processes in the GSI itself. In this paper, the development of the tangent linear and adjoint versions of the GSI are discussed and observation sensitivity results for a near-operational version of the system are shown. Results indicate that the GSI adjoint provides accurate assessments of the sensitivities with respect to observations of wind, temperature, satellite radiances, and, to a lesser extent, moisture. Sensitivities with respect to ozone observations are quite linear for the ozone fields themselves, but highly nonlinear for other variables. The sensitivity information provided by the adjoint is used to estimate the contribution, or impact, of various observing systems on locally defined response functions based on the analyzed increments of temperature and zonal wind. It is shown, for example, that satellite radiances have the largest impact of all observing systems on the temperature increments over the eastern North Pacific, while conventional observations from rawinsondes and aircraft dominate the impact on the zonal wind increments over the continental United States. The observation impact calculations also provide an additional means of validating the observation sensitivities produced by the GSI adjoint.

Radiance bias correction is an important and necessary step in the proper use of satellite observations in a data assimilation system. The original radiance bias‐correction scheme used in the Gridpoint Statistical Interpolation (GSI) data assimilation system consists of two components: a variational air‐mass dependent component and a scan‐angle component. The air‐mass component is updated within the GSI, while the scan‐angle component is updated outside the GSI. This study examines and enhances several aspects of the radiance bias‐correction problem. First, a modified pre‐conditioning is applied to the bias‐correction coefficients and the analysis variables to speed up convergence of the minimization process. A new procedure for applying the modified pre‐conditioning in the GSI is utilized. Second, capabilities for detecting any new/missing/recovering radiance data and initializing the bias correction for new radiance data are implemented. A new scheme is proposed and employed to adjust the background‐error variances for the bias‐correction coefficients automatically, using an approximation of the analysis‐error variances from the previous cycle, and to remove the pre‐specified predictor scaling parameters. Finally, the capability to perform bias correction for passive channels within the GSI is developed with a new approach. The two‐step bias‐correction procedure originally used is replaced with a one‐step variational bias‐correction scheme within the GSI. Experiment results with the GSI‐based hybrid ensemble‐variational system show that using the modified pre‐conditioning leads to a better convergence rate. Moreover, with the one‐step scheme, the anomaly correlation of geopotential height at 500 mb is neutral in the Northern Hemisphere but improved in the Southern Hemisphere. The root‐mean‐square (RMS) error of wind is comparable to that of the two‐step scheme and the biases of the global temperature 24 h and 48 h forecasts fitted to the rawinsonde are reduced.

A B S T R A C TWith the adjoint of a data assimilation system, the impact of any or all assimilated observations on measures of forecast skill can be estimated accurately and efficiently. The approach allows aggregation of results in terms of individual data types, channels or locations, all computed simultaneously. In this study, adjoint-based estimates of observation impact are compared with results from standard observing system experiments (OSEs) using forward and adjoint versions of the NASA GEOS-5 atmospheric data assimilation system. Despite important underlying differences in the way observation impacts are measured in the two approaches, the results show that they provide consistent estimates of the overall impact of most of the major observing systems in reducing a dry total-energy metric of 24-h forecast error over the globe and extratropics and, to a lesser extent, over the tropics. Just as importantly, however, it is argued that the two approaches provide unique, but complementary, information about the impact of observations on numerical weather forecasts. Moreover, when used together, they reveal both redundancies and dependencies between observing system impacts as observations are added or removed from the data assimilation system. Understanding these dependencies appears to pose an important challenge in making optimal use of the global observing system for numerical weather prediction.

Motived by experimentally synthesized (Hong Y. L. et al., Science, 369 (2020) 670), the intrinsic piezoelectricity in monolayer ( , W, Cr, Ti, Zr and Hf) are studied by density functional theory (DFT). Among the six monolayers, has the best piezoelectric strain coefficient d 11 of 1.24 pm/V, and the second is 1.15 pm/V for . Taking as a example, strain engineering is applied to improve d 11. It is found that tensile biaxial strain can enhance d 11 of , and the d 11 at 4% strain can improve by 107% with respect to the unstrained one. By replacing the N by P or As in , the d 11 can be raised substantially. For and , the d 11 is as high as 4.93 pm/V and 6.23 pm/V, which is mainly due to smaller and very small minus or positive ionic contribution to piezoelectric stress coefficient e 11 with respect to . The discovery of this piezoelectricity in monolayer enables active sensing, actuating and new electronic components for nanoscale devices, and is recommended for experimental exploration.