Abstract

Cloud particles have different shapes in the atmosphere. Research on cloud particle shapes plays an important role in analyzing the growth of ice crystals and the cloud microphysics. To achieve an accurate and efficient classification algorithm on ice crystal images, this study uses image-based morphological processing and principal component analysis to extract features of images and apply intelligent classification algorithms for the Cloud Particle Imager (CPI). Currently, there are mainly two types of ice-crystal classification methods: one is the mode parameterization scheme, and the other is the artificial intelligence model. Combined with data feature extraction, the dataset was tested on 10 types of classifiers, and the highest average accuracy was 99.07%. The fastest processing speed of the real-time data processing test was 2000 images per second. In actual application, the algorithm should consider the processing speed, because the images are on the order of millions. Therefore, a support vector machine (SVM) classifier was used in this study. The SVM-based optimization algorithm can classify ice crystals into nine classes with an average accuracy of 95%, blurred frame accuracy of 100%, with a processing speed of 2000 images per second. This method has a relatively high accuracy and faster classification processing speed than the classic neural network model. The new method could be also applied in physical parameter analysis of cloud microphysics.

Abstract

The Madden–Julian oscillation (MJO) is a fundamental mode of the tropical atmosphere variability that exerts significant influence on global climate and weather systems. Current global circulation models, unfortunately, are incapable of robustly representing this form of variability. Meanwhile, a well-accepted and comprehensive theory for the MJO is still elusive. To help address this challenge, recent emphasis has been placed on characterizing the vertical structures of the MJO. In this study, the authors analyze vertical heating structures by utilizing recently updated heating estimates based on the Tropical Rainfall Measuring Mission (TRMM) from two different latent heating estimates and one radiative heating estimate. Heating structures from two different versions of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses/forecasts are also examined. Because of the limited period of available datasets at the time of this study, the authors focus on the winter season from October 1998 to March 1999.

The results suggest that diabatic heating associated with the MJO convection in the ECMWF outputs exhibits much stronger amplitude and deeper structures than that in the TRMM estimates over the equatorial eastern Indian Ocean and western Pacific. Further analysis illustrates that this difference might be due to stronger convective and weaker stratiform components in the ECMWF estimates relative to the TRMM estimates, with the latter suggesting a comparable contribution by the stratiform and convective counterparts in contributing to the total rain rate. Based on the TRMM estimates, it is also illustrated that the stratiform fraction of total rain rate varies with the evolution of the MJO. Stratiform rain ratio over the Indian Ocean is found to be 5% above (below) average for the disturbed (suppressed) phase of the MJO. The results are discussed with respect to whether these heating estimates provide enough convergent information to have implications on theories of the MJO and whether they can help validate global weather and climate models.

Abstract

We compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models [including those participating in phase 6 of the Coupled Model Intercomparison Project (CMIP)] compared to previous versions. In particular, we examine the representation of the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the quasi-biennial oscillation (QBO) in the tropical stratosphere, and the dominant modes of extratropical variability, including the southern annular mode (SAM), the northern annular mode (NAM) [and the closely related North Atlantic Oscillation (NAO)], and the Pacific–North American pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g., the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

A multiscale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach for climate modeling. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The CRM allows for explicit simulation of cloud processes and their interactions with radiation and surface processes, and the GCM allows for global coverage.

A new MMF has been developed that is based on the NASA Goddard Space Flight Center (GSFC) finite-volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using traditional cloud parameterizations. Both MMFs also produce a large and positive precipitation bias in the Indian Ocean and western Pacific during the Northern Hemisphere summer. However, there are also notable differences between the two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF simulates more global rainfall than its parent GCM because of the high contribution from the oceanic component. A number of critical issues (i.e., the CRM's physical processes and its configuration) involving the Goddard MMF are discussed in this paper.

Abstract

The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core—the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25 km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the stochastically perturbed physics tendencies (SPPT) scheme and stochastic kinetic energy backscatter (SKEB) scheme. Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error–spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over North America. GEFSv12 was implemented on 23 September 2020.

Abstract

Upper Brahmaputra (UB) is the largest (∼240,000 km²) river basin of the Tibetan Plateau, where hydrological processes are highly sensitive to climate change. However, constrained by difficult access and sparse in situ observations, the variations in precipitation, glaciers, frozen ground, and vegetation across the UB basin remain largely unknown, and consequently the impacts of climate change on streamflow cannot be accurately assessed. To fill this gap, this project aims to establish a basinwide, large-scale observational network (that includes hydrometeorology, glacier, frozen ground, and vegetation observations), which helps quantify the UB runoff processes under climate–cryosphere–vegetation changes. At present, a multisphere observational network has been established throughout the catchment: 1) 12 stations with custom-built weighing automatic rain/snow meters and temperature probes to obtain elevation-dependent gradients; 2) 9 stations with soil moisture/temperature observations at four layers (10, 40, 80, 120 cm) covering Alpine meadow, grasslands, shrub, and forest to measure vegetation (biomass and vegetation types) and soil (physical properties) simultaneously; 3) 34 sets of probes to monitor frozen ground temperatures from 4,500 to 5,200 m elevation (100-m intervals), and two observation systems to monitor water and heat transfer processes in frozen ground at Xuegela (5,278 m) and Mayoumula (5,256 m) Mountains, for improved mapping of permafrost and active layer characteristics; 4) 5 sets of altimetry discharge observations along ungauged cross sections to supplement existing operational gauges; 5) high-precision glacier boundary and ice-surface elevation observations at Namunani Mountain with differential GPS, to supplement existing glacier observations for validating satellite imagery. This network provides an excellent opportunity to monitor UB catchment processes in great detail.

In 1997, during the late stages of production of NCEP–NCAR Global Reanalysis (GR), exploration of a regional reanalysis project was suggested by the GR project's Advisory Committee, “particularly if the RDAS [Regional Data Assimilation System] is significantly better than the global reanalysis at capturing the regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.” Following a 6-yr development and production effort, NCEP's North American Regional Reanalysis (NARR) project was completed in 2004, and data are now available to the scientific community. Along with the use of the NCEP Eta model and its Data Assimilation System (at 32-km–45-layer resolution with 3-hourly output), the hallmarks of the NARR are the incorporation of hourly assimilation of precipitation, which leverages a comprehensive precipitation analysis effort, the use of a recent version of the Noah land surface model, and the use of numerous other datasets that are additional or improved compared to the GR. Following the practice applied to NCEP's GR, the 25-yr NARR retrospective production period (1979–2003) is augmented by the construction and daily execution of a system for near-real-time continuation of the NARR, known as the Regional Climate Data Assimilation System (R-CDAS). Highlights of the NARR results are presented: precipitation over the continental United States (CONUS), which is seen to be very near the ingested analyzed precipitation; fits of tropospheric temperatures and winds to rawinsonde observations; and fits of 2-m temperatures and 10-m winds to surface station observations. The aforementioned fits are compared to those of the NCEP–Department of Energy (DOE) Global Reanalysis (GR2). Not only have the expectations cited above been fully met, but very substantial improvements in the accuracy of temperatures and winds compared to that of GR2 are achieved throughout the troposphere. Finally, the numerous datasets produced are outlined and information is provided on the data archiving and present data availability.

Abstract

For the newly implemented Global Ensemble Forecast System, version 12 (GEFSv12), a 31-yr (1989–2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System, version 15.1, and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–99) and Phase 2 (2000–19) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast dataset was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500-hPa geopotential height, tropical storm track, precipitation, 2-m temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.

Abstract

As the second-largest shifting sand desert worldwide, the Taklimakan Desert (TD) represents the typical aeolian landforms in arid regions as an important source of global dust aerosols. It directly affects the ecological environment and human health across East Asia. Thus, establishing a comprehensive environment and climate observation network for field research in the TD region is essential to improve our understanding of the desert meteorology and environment, assess its impact, mitigate potential environmental issues, and promote sustainable development. With a nearly 20-yr effort under the extremely harsh conditions of the TD, the Desert Environment and Climate Observation Network (DECON) has been established completely covering the TD region. The core of DECON is the Tazhong station in the hinterland of the TD. Moreover, the network also includes 4 satellite stations located along the edge of the TD for synergistic observations, and 18 automatic weather stations interspersed between them. Thus, DECON marks a new chapter of environmental and meteorological observation capabilities over the TD, including dust storms, dust emission and transport mechanisms, desert land–atmosphere interactions, desert boundary layer structure, ground calibration for remote sensing monitoring, and desert carbon sinks. In addition, DECON promotes cooperation and communication within the research community in the field of desert environments and climate, which promotes a better understanding of the status and role of desert ecosystems. Finally, DECON is expected to provide the basic support necessary for coordinated environmental and meteorological monitoring and mitigation, joint construction of ecologically friendly communities, and sustainable development of central Asia.