Medium-range Numerical Weather Prediction Research Articles

Heavy rainfall forecasting for Dehradun capital city during monsoon season 2020

Dehradun city is Uttarakhand's capital and part of the doon valley, which lies between the Himalayas and the Shivaliks. India Meteorological Department has undertaken the Impact Based Forecasting (IBF) of heavy rainfall events for capital/major cities on an experimental basis from the monsoon season 2020. With the improvement in meteorological observation & monitoring network of Uttarakhand and short & medium range Numerical Weather Prediction (NWP) model outputs, it has become possible to provide impact-based forecast at a very small spatial scale with good accuracy. With the help of a dense network of 5 General Packet Radio Service (GPRS) based Automatic Weather Stations (AWS) & 2 Automatic Rain Gauges (ARG) within a 20 Km radius of Dehradun city center, it has become possible to capture & monitor these events. The spatial analysis of these events suggests that the local orographic features rather than large-scale synoptic features played a major role in heavy rainfalls over Dehradun city. The Heidke skill score of 0.63 and 100% probability of detection is found for the capital city forecast of Dehradun. The presence of monsoon trough to the north of its normal position, a cyclonic circulation south of Uttarakhand over Haryana or East-Rajasthan or West Uttar Pradesh and a Western disturbance around 70°E were found to be the synoptic systems favorable for the occurrence of heavy rainfall in Dehradun city. The occasional strong south-westerly wind flow from the Arabian Sea converging over the foothills of the Western Himalayas is also found to be responsible for the heavy downpour.

FENGYUN-4A Advanced Geosynchronous Radiation Imager Layered Precipitable Water Vapor Products’ Comprehensive Evaluation Based on Quality Control System

A physical retrieval algorithm has been developed for deriving the layered precipitable water vapor (LPWs) product from infrared radiances of the Advanced Geosynchronous Radiation Imager (AGRI) onboard FengYun-4A (FY-4A), the first of the new generation of Chinese geostationary weather satellites (FengYun-4, or FY-4 Series). The FY-4A AGRI LPWs are evaluated with different types of reference datasets based on Quality Control System (QCS), including those from Himawari-8 AHI (Advanced Himawari Imager), MODIS (Moderate Resolution Imaging Spectroradiometer), Radiosonde, ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5), NCEP (National Centers for Environmental Prediction) reanalysis and CMA (China Meteorological Administration) forecast product from global medium range numerical weather prediction (NWP) system. QCS is one of the important components of FY-4A ground segment, which mainly focuses on the satellite products’ evaluation and validation. It is found that the AGRI LPW product has a good agreement with different evaluating sources and the quality is favorable and stable. With the capability of frequent (5-min interval) observations over the East Asia and Western Pacific regions, the AGRI LPW products can be used to investigate the atmospheric temporal and spatial variations in the pre-landfall environment for typhoons. The QCS is a useful tool to monitor, evaluate, and validate the AGRI LPW products.

Sensitivity of Tropical Tropopause Layer Cirrus Prediction in GRAPES Global Forecast System

Abstract A warm bias with a maximum value of over 4 K in the tropical tropopause layer (TTL) is detected in day-5 operational forecasts of the Global/Regional Assimilation and Prediction System (GRAPES) for global medium-range numerical weather prediction (GRAPES_GFS). In this study, the predicted temperature changes caused by different processes are examined, and the predicted cloud fractions are compared with the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis data. It is found that the overprediction of the TTL cirrus fraction contributes to the warm bias due to cloud-radiative heating. The interactions among the ice nucleation, deposition/sublimation, and the large-scale condensation together determine the results of the TTL ice crystal content prediction. Moreover, a range of sensitivity experiments show that the TTL ice crystal content prediction is sensitive to the threshold relative humidity over ice (RHi) in the ice nucleation process. Then the uncertainties of the formulas for saturation vapor pressure over ice at very low temperatures are discussed. The RHi calculated based on the Magnus–Tetens formula is up to 10% higher than that based on the Goff–Gratch formula. As the Goff–Gratch formula is applicable over a broader range of 184–273 K, it is more suitable for the cold TTL. When the Goff–Gratch formula rather than the Magnus–Tetens formula is used in the microphysics scheme, the TTL cirrus forecasts are improved greatly, and the warm bias disappears completely. After investigating the interplay of the dynamical, microphysical, and radiative processes, we find a positive feedback mechanism that exacerbates the TTL cirrus prediction error.

Multiscale Global Atmosphere Model SL-AV: the Results of Medium-range Weather Forecasts

Development of the multiscale version of the global atmosphere model SL-AV required many improvements in the dynamical core, replacement or refinement of parameterization algorithms and complex tuning of the model. These modifications were initially tested with the experiments on modern climate simulation and then incorporated into the model configuration for medium-range numerical weather prediction. The impact of these model improvements on forecast quality is studied in this paper. The increase in accuracy of model climate characteristics has led to the reduction of forecast errors. The comparison of quality for numerical forecasts starting from the initial data of Hydrometcenter of Russia and ECMWF is carried out. The effect of replacing the initial data turned out to be comparable to the effect of multi-year works on model development. This shows the importance and necessity of development and improvement of the Hydrometcenter of Russia data assimilation system.

Development of an instant correction and display system of numerical weather prediction products in China

This paper presents the development of numerical prediction products (NPP) correction and display system (NPPCDS) for rapid and effective post-processing and displaying of the T213 NPP (numerical prediction products of the medium range numerical weather prediction spectral model T213L31) through instant correction method. The NPPCDS consists of two modules: an automatic correction module and a graphical display module. The automatic correction module automatically corrects the T213 NPP at regularly scheduled time intervals, while the graphical display module interacts with users to display the T213 NPP and its correction results. The system helps forecasters extract the most relevant information at a quick glance without extensive post-processing. It is simple, easy to use, and computationally efficient, and has been running stably at Huludao Meteorological Bureau in Liaoning Province of China for the past three years. Because of its low computational costs, it is particularly useful for meteorological departments that lack advanced computing capacity and still need to make short-range weather forecasting.

A study on height reassignment for the AMV products of the FY-2C satellite

A method for using height reassignment to improve the quality of satellite-derived atmospheric motion vectors (AMVs) is presented. The rationale underlying height reassignment is explored, and the technical details are studied by applying three height reassignment schemes that use NCEP reanalysis winds. The quality of the AMVs is generally improved following reassignment, although the magnitude of the improvement differs according to the scheme applied. Scheme 3 provides the best quality and stability, followed by Scheme 1 and Scheme 2. The negative biases in the zonal components of the AMVs decrease from [−5, −4] m s−1 to <−1 m s−1 following reassignment. The meridional components also improve. The AMVs derived from the infrared and water vapor channels improve by 58.7% and 25%, respectively. The feasibility of using Scheme 3 in the operational derivation of AMVs is studied by incorporating the forecast wind field predicted by a T511 medium-range numerical weather prediction (NWP) system. Incorporating the 12-h forecast reduces the negative biases in zonal winds and positive biases in meridional winds retrieved from the water vapor channel, improving the overall quality of the AMVs by 26.7%. Extending the validity period of the forecast field linearly reduces the improvement in retrieved AMVs, but the magnitude of this reduction is small. Incorporating the 120-h forecast field still results in a 13% improvement, although it may eliminate a larger number of AMVs of good quality.

The Utility of Upper-Boundary Nesting in NWP

Abstract The importance of stratospheric influences for medium-range numerical weather prediction (NWP) of the troposphere has led to increases in the heights of global model domains at operational centers around the world. Grids now routinely extend to 0.1 hPa (approximately 65 km) in these systems, thereby covering the full depth of the stratosphere and the lower portion of the mesosphere. Increasing the vertical extent of higher-resolution limited-area models (LAMs) nested within the global forecasts is problematic because of the computational cost of additional levels and the possibility of inaccuracy or instability in the high-speed stratospheric jets. An upper-boundary nesting (UBN) technique is developed that allows information from high-topped driving grids to influence the evolution of a lower-topped (~10 hPa) LAM integration in a manner analogous to the treatment of lateral boundary conditions. A stratospheric vortex displacement event in the winter of 2007 is used to study the effectiveness of the UBN technique. Tropospheric blocking over Europe leads to the development of an amplifying planetary-scale wave in the lower stratosphere that culminates in an anticyclonic wave break over Asia and a marked increase of wave-1 asymmetry. The rapid evolution of stratospheric potential vorticity (PV) is poorly represented in low-topped models, resulting in PV-induced forecast height errors throughout the depth of the troposphere on time scales as short as 2–5 days. Application of the UBN technique is shown to be an effective way for low-topped configurations to benefit from stratospheric predictability without the problems associated with the inclusion of the stratospheric flow in the higher-resolution model domain. The robustness and relative ease of implementation of the UBN technique may make this computationally inexpensive strategy attractive for a wide range of NWP applications.

MEETING SUMMARIES

What: Forty forecasters, researchers, and others focused on high-latitude numerical modeling discuss recent results and prospects for an Arctic System Reanalysis When: October 2003 Where: International Arctic Research Center, University of Alaska at Fairbanks, Fairbanks, Alaska he workshop on Short to Medium Range Numerical Weather Prediction in the Arctic and Antarctic fostered interaction among researchers and operational forecasters working in highlatitude numercial weather prediction (NWP) and promoted an eff ort toward an Arctic System Reanalysis (ASR), proposed at an earlier workshop (Overland et al. 2003). Th e attendees provided a variety of operational and research perspectives as well as energetic discussion on current results and the proposed ASR.

Scale Interactions and Atmospheric Predictability: An Updated Perspective

An examination of the scale interactions in predictability experiments is made using the NCAR Community Climate Model Version 3 (CCM3) at various horizontal resolutions ranging from T42 to T170. Both identicalmodel and imperfect-model twin experiments are analyzed, and they show distinctive differences from the classical inverse cascade picture of predictability error growth. In the identical-model twin framework, error growth experiments using initial errors confined to long and short scales are compared and contrasted. In these cases, error growth eventually asymptotes to an exponential growth of baroclinically active scales. In the imperfect-model twin experiments, errors rapidly disperse from scales technically beyond model resolution to a small amplitude, spectrally uniform distribution of errors in resolved scales. The errors in resolved scales further amplify in a quasi-exponential growth of the baroclinically active scales. Finally, the implications of these growth mechanisms for the necessary resolution in short- to medium-range numerical weather prediction are given under the assumption that the accuracy of current initial state estimates of the atmosphere remain fixed at their present level.

Use of Medium-Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3°C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases the accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts. Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado; East Fork of the Carson River near Gardnerville, Nevada; and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as “truth” to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow.

A new general circulation model: formulation and preliminary results in a single- and multi-processor environment

This article describes a new general circulation model (GCM) developed jointly by The University of New South Wales (UNSW) and the University of Hamburg. The model is versatile in that it can be run as a medium-range (1 to 15 days) global numerical weather prediction (NWP) model; as an extended range (15 to 30 days) NWP model; and as a GCM for periods extending from seasons, through annual and decadal periods, and beyond. The model can be coupled with ocean models that vary in complexity from simple "swamp" oceans to complex ocean GCMs. The atmospheric GCM also has a number of novel features, particularly in the numerical integration scheme which is a high-order, mass-conserving, semi-implicit semi-Lagrangian scheme, thereby removing the stability restriction on the time-step and allowing efficient long-term integrations. The emphasis here will be on demonstrating that the new model performs effectively on the usual measures of skill (statistics such as mean errors, root-mean-square errors and anomaly correlations) in several standard applications upon which new models usually are assessed. These applications include medium range weather forecasts out to 10 days on a daily basis over a one year period; a limited 10-year simulation climatology, prediction of atmospheric anomalies using SST anomalies in an El Nino year; and an alternative two-way approach to regional modelling (the "down-scaling problem") made possible because the unconditional stability of the semi-implicit, semi-Lagrangian formulation permits large variations in grid spacing without changing the time step size. Finally, the model is run on a variety of parallel computing platforms and it is shown that near-linear speed-up can be attained. This is significant for both medium range NWP and very long-term GCM integrations.

Parallel computing of the global spectral atmospheric model on the YH-2 supercomputer

A global spectral atmospheric model has been vectorized and multitasked on the YH-2 supercomputer. The model is used for the operational system of medium-range numerical weather prediction in National Meteorological Center (NMC), China. In this paper the vectorization algorithms of the spectral-grid transformation and multitasking schemes of the model are discussed in detail. The results show that high speed-up for the model can be obtained.

A Normal-Mode Analysis of Rapid Teleconnections in a Numerical Weather Prediction Model. Part I: Global Aspects

Global-scale interactions between the tropics and extratropics are investigated using a version of US Navy's global operational numerical weather prediction model. The primary goals of this study are (1) to demonstrate the importance of atmospheric teleconnections for medium-range numerical weather prediction and (2) to analyze the evolution and dynamic structure of the response in a sophisticated numerical forecast model. The model normal modes are used as the principal diagnostic tool for analyzing the response to sea surface temperature anomalies in the tropical Pacific. By monitoring the energy growth in the dominant horizontal and vertical modes and comparing these with conventional difference-field diagnostics, it is shown that the character of the long-term response is well established within one, to two weeks after the heating anomaly is introduced. The growth rates and structures of these modes provide insights into the dynamic processes that control the model response. In the tropics, enhanced convection is clearly the dominant forcing mechanism for these modes. In the extratropics, a more complicated picture arises in which both meridionally propagating energy and in situ instabilities in the ambient flow appear to be important mechanisms for producing the observed wave patterns. The results clearly demonstrate that tropical forcing canmore » have a significant global impact on time scales relevant to medium-range numerical weather prediction. In Part 11 of this study, the normal-mode diagnostic approach is extended by developing a technique for partitioning the modes according to their latitudinal variances in order to examine the tropical and extratropical responses in further detail. It is shown that the modes are a powerful and flexible tool for diagnosing the behavior of a complicated model.« less