National Centers For Environmental Prediction Global Forecast System Research Articles

A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event

ABSTRACT We integrated Enviro-HIRLAM (Environment-High Resolution Limited Area Model) meteorological output into FLEXPART (FLEXible PARTicle dispersion model). A FLEXPART simulation requires meteorological input from a numerical weather prediction (NWP) model. The publicly available version of FLEXPART can utilize either ECMWF (European Centre for Medium-range Weather Forecasts) Integrated Forecast System (IFS) forecast or reanalysis NWP data, or NCEP (U.S. National Center for Environmental Prediction) Global Forecast System (GFS) forecast or reanalysis NWP data. The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields. We compared backward trajectories generated with FLEXPART using Enviro-HIRLAM (both with and without aerosol effects) to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs, for a case study of a heavy haze event which occurred in Beijing, China in November 2018. We found that results from FLEXPART were considerably different when using different meteorological inputs. When aerosol effects were included in the NWP, there was a small but noticeable difference in calculated trajectories. Moreover, when looking at potential emission sensitivity instead of simply expressing trajectories as lines, additional information, which may have been missed when looking only at trajectories as lines, can be inferred.

The Impact of Dropsonde Data on the Performance of the NCEP Global Forecast System during the 2020 Atmospheric Rivers Observing Campaign. Part II: Dynamic Variables and Humidity

Abstract The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical weather forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the National Centers for Environmental Prediction (NCEP) Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble–variational (4DEnVar) data assimilation system. The control run (CTRL) used all of the routinely assimilated data and included data from 628 ARR dropsondes, whereas the denial run (DENY) excluded the dropsonde data. Results from 17 intensive observing periods (IOPs) indicate a mixed impact for mean sea level pressure and geopotential height over the Pacific–North American (PNA) region in CTRL compared to DENY. The overall local impact over the U.S. West Coast and Gulf of Alaska for the 17 IOPs is neutral (−0.45%) for integrated vapor transport (IVT), but positive for wind and moisture profiles (0.5%–1.0%), with a spectrum of statistically significant positive and negative impacts for various IOPs. The positive dropsonde data impact on precipitation forecasts over U.S. West Coast domains appears driven, in part, by improved low-level moisture and wind fields at short-forecast lead times. Indeed, data gaps, especially for accurate and unbiased moisture profiles and wind fields, can be at least partially mitigated to improve U.S. West Coast precipitation forecasts.

Verification of operational numerical weather prediction model forecasts of precipitation using satellite rainfall estimates over Africa

AbstractRainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate‐related diseases and public health. Numerical weather models (NWMs) are an important component of this work, due to their complete spatial coverage, high resolution and ability to forecast into the future. In this study, the spatio‐temporal skill of short‐term forecasts of rainfall across Africa from 2016 through 2018 is evaluated. Specifically, the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction‐Global Forecast System (NCEP‐GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in situ observations and are commonly used in analysis of African rainfall. Model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics of daily and weekly forecasts show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter‐tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at 1 week aggregation time are improved against daily forecasts.

Comparison Among All‐Sky Simulations, FY‐3E MWTS‐3 and FY‐4A AGRI Observations of the First Typhoon Malakas in 2022

AbstractThe newly launched early morning satellite Fengyun‐3E (FY‐3E) helps to form a three‐orbit constellation for better observing the first Typhoon Malakas in 2022. Together with MetOp‐B and NOAA‐20, global observations are made available 6 times daily from three temperature sounders of MWTS‐3, AMSU‐A, and ATMS onboard FY‐3E, MetOp‐B, and NOAA‐20, respectively. Having channel frequencies much less than 200 GHz, brightness temperatures (TBs) at different sounding channels are linearly related to temperatures at different altitudes. This allows Malakas's warm cores to be retrieved from MWTS‐3, AMST‐A, and ATMS TB observations. The warm‐core maxima of Malakas at 250 hPa has a single‐peaked diurnal cycle, with its maximum and minimum peaking around midnight and noon, respectively. FY‐3E MWTS‐3 observations allowed the intensity and phase of the diurnal cycle better captured. The diurnal variations of warm core retrieved from all‐sky TB simulations of the ERA5 reanalysis and National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis compared well with the three‐orbit constellation retrieval. All‐sky simulations of TB from the NCEP GFS analysis compared more favorably with FY‐4A AGRI TB observations than those from the ERA5 reanalysis.

Evaluating the added value of multi-input atmospheric transport ensemble modeling for applications of the Comprehensive Nuclear Test-Ban Treaty organization (CTBTO)

The Comprehensive Nuclear Test-Ban Treaty Organization (CTBTO) runs to date operationally an atmospheric transport modeling chain in backward mode based on operational deterministic European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System (ECMWF-IFS) and on National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) input data. Meanwhile, ensemble dispersion modeling is becoming more and more widespread due to the ever increasing computational power and storage capacities. The potential benefit of this approach for current and possible future CTBTO applications was investigated using data from the ECMWF-Ensemble Prediction System (EPS). Five different test cases - among which are the ETEX-I experiment and the Fukushima accident - were run in backward or forward mode and - in the light of a future operational application - special emphasis was put on the performance of an arbitrarily selected 10- versus the full 51-member ensemble. For those test cases run in backward mode and based on a puff release it became evident that Possible Source Regions (PSRs) can be meaningfully reduced in size compared to results based solely on the deterministic run by applying minimum and probability of exceedance ensemble metrics. It was further demonstrated that a given puff release of 4E10 Bq of Se-75 can be reproduced within the meteorological uncertainty range [1.9E9 Bq,1.7E13 Bq] including a probability for not exceeding an assumed upper limit source term using simple scaling of a measurement with the corresponding ensemble metrics of backward fields. For the test cases run in forward mode it was found that the control run as well as 10- and 51-member medians all exhibit similar performance in time series evaluation. Maximum rank difference adds up to less than 10% with reference to possible rank values [0,4]. The maximum difference in the Brier score for both ensembles is less than 3%. The main added value of the ensemble lies in producing meteorologically induced concentration uncertainties and thus explaining observed measurements at specific sites. Depending on the specific test case and on the ensemble size between 27 and 74% of samples all lie within concentration ranges derived from the different meteorological fields used. In the future uncertainty information per sample could be used in a full source term inversion to account for the meteorological uncertainty in a proper way. It can be concluded that a 10-member meteorological ensemble is good enough to already benefit from useful ensemble properties. Meteorological uncertainty to a large degree is covered by the 10-member subset because forecast uncertainty is largely suppressed due to concatenating analyses and short term forecasts, as required in the operational CTBTO procedure, on which this study focuses. Besides, members from different analyses times are on average unrelated. It was recommended to Working Group B of CTBTO to implement the ensemble system software in the near future.

Predictability of PM2.5 in Seoul based on atmospheric blocking forecasts using the NCEP global forecast system

We investigate the predictability of PM2.5 (particulate matter with diameters ≤ 2.5 μm) concentration in Seoul using atmospheric blocking forecasts during four winter seasons (December, January, and February) of 2015–2019. Atmospheric blocking is predicted using the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), and PM2.5 concentration in Seoul of the same period are obtained from the surface measurements of the National Institute of Environmental Research (NIER).We define blocking indices using two methods: the anomaly method and the gradient method. Correlation analysis establishes the relationship between the blocking and PM2.5 concentrations in Seoul. The result based on the anomaly method exhibits strong positive and negative correlation coefficients respectively over the East Sea and Japan and the vicinity of Lake Baikal. The correlation coefficient from the blocking index of the gradient method reveals a similar pattern, and the maximum values are found within the longitude range of 130–150°E Building on the relationship between PM2.5 in Seoul and the blocking index, we perform PM2.5 prediction using the GFS forecast of the atmospheric blocking index. The prediction shows reliable performance at least up to a five-day forecast. This atmospheric blocking based dynamical and statistical PM2.5 prediction can be a useful tool for mid-range PM2.5 forecast.

Global Forecast Impact of Low Data Latency Infrared and Microwave Sounders Observations from Polar Orbiting Satellites

The Direct Broadcast Network (DBNet) provides near-real-time delivery of low-earth-orbiting (LEO) meteorological satellites to operational numerical weather prediction (NWP) systems that need short data cut-off times to allow for the assimilation of the most recent satellite measurements. The NWP model requires timely delivery of observations including atmospheric temperature, humidity, and surface wind vectors. The World Meteorological Organization (WMO) Space Program (WSP) recommends the data latency of no more than 20 min for the satellite measurements. Currently, not all DBNet stations are delivering satellite data within the 20-min time frame. In this study, the forecast impact of the observations of LEO satellite sounders with data latency of 20 min or less was evaluated using the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Reducing the data latency up to 5 min increases the number of LEO infrared (IR) and microwave (MW) sounder observations delivered to the NCEP GFS data assimilation system by more than 20%. Overall, this study demonstrates a positive impact on the global weather forecasts when the IR and MW sounder data are delivered by 20 min anywhere in the world. Additional forecast benefits are not obvious for shorter data latency. Results from this study support the WSP recommendation of 20–minute data latency.

Sensitivities of the NCEP Global Forecast System

Abstract An important issue in developing a forecast system is its sensitivity to additional observations for improving initial conditions, to the data assimilation (DA) method used, and to improvements in the forecast model. These sensitivities are investigated here for the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP). Four parallel sets of 7-day ensemble forecasts were generated for 100 forecast cases in mid-January to mid-March 2016. The sets differed in their 1) inclusion or exclusion of additional observations collected over the eastern Pacific during the El Niño Rapid Response (ENRR) field campaign, 2) use of a hybrid 4D–EnVar versus a pure EnKF DA method to prepare the initial conditions, and 3) inclusion or exclusion of stochastic parameterizations in the forecast model. The Control forecast set used the ENRR observations, hybrid DA, and stochastic parameterizations. Errors of the ensemble-mean forecasts in this Control set were compared with those in the other sets, with emphasis on the upper-tropospheric geopotential heights and vorticity, midtropospheric vertical velocity, column-integrated precipitable water, near-surface air temperature, and surface precipitation. In general, the forecast errors were found to be only slightly sensitive to the additional ENRR observations, more sensitive to the DA methods, and most sensitive to the inclusion of stochastic parameterizations in the model, which reduced errors globally in all the variables considered except geopotential heights in the tropical upper troposphere. The reduction in precipitation errors, determined with respect to two independent observational datasets, was particularly striking.

Dynamical downscaling the impact of spring Western US land surface temperature on the 2015 flood extremes at the Southern Great Plains: effect of domain choice, dynamic cores and land surface parameterization

Recent studies have shown that spring land surface temperature (LST) and subsurface temperature (SUBT) over the high elevation areas in the western US (WUS) have significant impacts on the downstream summer droughts/floods in North America. In this paper, both the National Centers for Environmental Prediction—Global Forecast System (NCEP-GFS) general circulation model (GCM) and the weather research and forecasting (WRF) regional climate model (RCM) are employed, where RCM scenarios utilized initial and lateral boundary conditions derived from the corresponding NCEP-GFS scenarios. Here we use a late spring flood in the US Southern Great Plains (SGP) case to examine whether simulation of the LST/SUBT downstream effects is sensitive to the domain size choice, change in dynamical cores within the same model, as well as to the representation of surface processes parameterizations. Although all RCM experiments with different settings simulate reasonably geographical patterns of observed LST and precipitation anomalies, we found that the choice of the domain size is crucial for proper downscaling the LST/SUBT downstream effects to accurately produce the observed precipitation/LST anomalies over the SGP/WUS, respectively, along with the associated large-scale features. The southern boundary location has been identified to be crucial in producing the SGP Low Level Jet strength, which in turn brings more moisture from the Gulf of Mexico to the SGP and thereby resulting in a better simulation of the precipitation anomaly in that area. The sensitivity of the simulation of the LST/SUBT downstream effect to dynamical cores is assessed by inter-comparing the Non-hydrostatic Mesoscale Model (NMM) and the Advanced Research WRF dynamic cores. We find NMM was better at generating the large-scale eastward wave train, a crucial process associated with the LST/SUBT downstream effect. Meanwhile, this study also shows that the LST/SUBT downstream effects were not significantly dependent on the surface process parameterizations, although the Simplified Simple Biosphere model version 3 (SSiB3) highlighted a better performance over SSiB2.

R2O Transition of NCAR’s Icing and Turbulence Algorithms into NCEP’s Operations

National Center for Environmental Prediction (NCEP) started distributing global operational gridded in flight icing, turbulence and convective cloud products as part of World Area Forecast System (WAFS) products in 2007. Simple algorithms were used to derive these products during early stage based on NCEP Global Forecast System (GFS) forecast. These products quickly became essential flight planning tool for international aviation community and are especially important to developing countries that do not have resource to run numerical models themselves. To further improve these products, Environmental Modeling Center (EMC) started collaborating with National Center for Atmospheric Research (NCAR) to transition their aviation research algorithms into NCEP’s operations (R2O), particularly Forecast Icing Potential (FIP) and Graphical Turbulence Guidance (GTG) algorithms. The initial attempt is to apply FIP to GFS forecast to potentially replace WAFS icing product. Extensive evaluation demonstrated FIP outperformed original WAFS icing product and, with support from Aviation Weather Center (AWC) and Federal Aviation Administration (FAA), EMC replaced US WAFS icing product with FIP in 2015. EMC recently also implemented GTG with 2017 GFS upgrade but GTG will not replace WAFS turbulence until 2019. This paper will describe the methodology which EMC used to transition NCAR’s aviation research algorithms into NCEP’s operations. It will also describe how EMC generates icing analysis data to be used as truth for performing objective verification. Several case studies will be presented and the methodology and results for objective validation will be discussed. Finally, future collaboration plan with NCAR and implementation plans to continue to improve WAFS products will be stated.

Prediction of heavy rainfall over Chennai Metropolitan City, Tamil Nadu, India: Impact of microphysical parameterization schemes

This study attempts to investigate the real-time prediction of a heavy rainfall event over the Chennai Metropolitan City, Tamil Nadu, India that occurred on 01 December 2015 using Advanced Research Weather Research and Forecasting (WRF-ARW) model. The study evaluates the impact of six microphysical (Lin, WSM6, Goddard, Thompson, Morrison and WDM6) parameterization schemes of the model on prediction of heavy rainfall event. In addition, model sensitivity has also been evaluated with six Planetary Boundary Layer (PBL) and two Land Surface Model (LSM) schemes. Model forecast was carried out using nested domain and the impact of model horizontal grid resolutions were assessed at 9km, 6km and 3km. Analysis of the synoptic features using National Center for Environmental Prediction Global Forecast System (NCEP-GFS) analysis data revealed strong upper-level divergence and high moisture content at lower level were favorable for the occurrence of heavy rainfall event over the northeast coast of Tamil Nadu. The study signified that forecasted rainfall was more sensitive to the microphysics and PBL schemes compared to the LSM schemes. The model provided better forecast of the heavy rainfall event using the logical combination of Goddard microphysics, YSU PBL and Noah LSM schemes, and it was mostly attributed to timely initiation and development of the convective system. The forecast with different horizontal resolutions using cumulus parameterization indicated that the rainfall prediction was not well represented at 9km and 6km. The forecast with 3km horizontal resolution provided better prediction in terms of timely initiation and development of the event. The study highlights that forecast of heavy rainfall events using a high-resolution mesoscale model with suitable representations of physical parameterization schemes are useful for disaster management and planning to minimize the potential loss of life and property.

Real-time Extremely Heavy Rainfall Forecast and Warning over Rajasthan During the Monsoon Season (2016)

Two events of extremely heavy rainfall occurred over Rajasthan during 7–9 August 2016 and 19–21 August 2016. Due to these events, flooding occurred over east Rajasthan and affected the normal life of people. A low-pressure area lying over northwest Madhya Pradesh on 7 August 2016 moved north-westward and lay near east Rajasthan and adjoining northwest Madhya Pradesh on 8 and 9 August 2016. Under the influence of this low-pressure system, Chittorgarh district and adjoining areas of Rajasthan received extremely heavy rainfall of 23 cm till 0300 UTC of 8 August 2016 and 34 cm on 0300 UTC of 9 August 2016. A deep depression lying over extreme south Uttar Pradesh and adjoining northeast Madhya Pradesh on 19 August 2016 moved westward and gradually weakened into a depression on 20 August 2016. It further weakened into a low-pressure area and lay over east Rajasthan on 21 and 22 August 2016. Under the influence of this deep depression, Jhalawar received 31 cm and Baran received 25 cm on 19 August. On 20 August 2016, extremely heavy rainfall (EHR) occurred over Banswara (23.5 cm), Pratapgarh (23.2 cm) and Chittorgarh (22.7 cm) districts. In this paper, the performance of the National Centers for Environmental Prediction (NCEP) global forecast system (GFS) model for real-time forecast and warning of heavy to very heavy/EHR that occurred over Rajasthan during 7–9 August 2016 and 19–21 August 2016 has been examined. The NCEP GFS forecast rainfall (Day 1, Day 2 and Day 3) was compared with the corresponding observed gridded rainfall. Based on the predictions given by the NCEP GFS model for heavy rainfall and with their application in real-time rainfall forecast and warnings issued by the Regional Weather Forecasting Center in New Delhi, it is concluded that the model has predicted the wind pattern and EHR event associated with the low-pressure system very accurately on day 1 and day 2 forecasts and with small errors in intensity and space for day 3.

Impact Assessment of Himawari-8 AHI Data Assimilation in NCEP GDAS/GFS with GSI

AbstractAs the first of the next-generation geostationary meteorological satellites, Himawari-8 was successfully launched in October 2014 by the Japan Meteorological Agency (JMA) and placed over the western Pacific Ocean domain at 140.7°E. It carries the Advanced Himawari Imager (AHI), which provides full-disk images of Earth at 16 bands in the visible and infrared domains every 10 min. Efforts are currently ongoing at the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS)/Center for Satellite Applications and Research (STAR) to assimilate Himawari-8 AHI radiance measurements into the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation analysis system (GSI). All software development within the GSI to allow for assimilation of Himawari-8 AHI radiance has been completed.This study reports on the assessment of AHI preassimilation data quality by comparing observed clear-sky ocean-only radiances to those simulated using collocated ECMWF analysis, as well as describing procedures implemented for quality control. The impact of the AHI data assimilation on the resulting analyses and forecasts is then assessed using the NCEP Global Forecast System (GFS). A preliminary assessment of the assimilation of AHI data from infrared water vapor channels and atmospheric motion vectors (AMVs) on top of the current global observing system shows neutral to marginal positive impact on analysis and forecast skill relative to an assimilation without AHI data. The main positive impact occurs for short- to medium-range forecasts of global upper-tropospheric water vapor. The results demonstrate the feasibility of direct assimilation of AHI radiances and highlight how humidity information can be extracted within the assimilation system.

Measurement, simulation, and meteorological interpretation of medium-range transport of radionuclides to Korea during the Fukushima Dai-ichi nuclear accident

Two radionuclides (131I and 137Cs) were measured and simulated over the Korean Peninsula during the Fukushima Dai-ichi nuclear accident, and the resulting meso-scale meteorological characteristics were explored in order to interpret the medium-range transport of radionuclides from the site of the Fukushima Dai-ichi nuclear accident to the Korean Peninsula. Peak concentrations of radionuclides were detected across all sites in Korea for a period of April 2–8, 2011, with maximum concentrations of 131I and 137Cs at 3.12mBq/m3 (on April 6), and 1.25mBq/m3 (on April 7), respectively; the highest levels on record in Korea since measurements began. The multi-particle Lagrangian model, the FLEXPART simulation based on the National Centers for Environmental Prediction/Global Forecast System (NCEP/GFS), successfully explored these high radionuclide peaks resulting from long- and medium-range transport processes from the accident site.The meteorological feature of the medium-range transport exploited in this study is the veering meso-scale circulation, in association with the presence of a blocking anticyclone, and its subsequent evolution with propagation eastwards over northeast Asia, which was one of the important factors in explaining the advection and redirection of radionuclides from the accident site to the Korean Peninsula. The blocking situation of the anticyclone, centered on the Korean Peninsula, lasted for two days (on April 4–5, 2011), and the northwesterlies and subsequent northerlies advected the radionuclides from the accident site to the southern Sea of Japan. They were then redirected towards Korea due to the veering circulations produced by the blocking anticyclone. The position-evolution of the anticyclone was concurrent with the timing of the transport of the highest level of 137Cs concentrations recorded in Korea. The vertical meteorological structure of the blocking anticyclone was also well featured to maintain its position due to the persistent compensation provided by being aloft a convergence zone, which is in good accordance with the location of the ridge on the 500hPa level.

Impacts of Including Rain-Evaporative Cooling in the Initial Conditions on the Prediction of a Coastal Heavy Rainfall Event during TiMREX

Abstract A cycling run, which began 36 h before the model forecast, was employed to assimilate special Terrain-influenced Monsoon Rainfall Experiment (TiMREX) soundings, Global Telecommunications System (GTS) data, and Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) global positioning system (GPS) radio occultation (RO) refractivity profiles to improve the model initial conditions provided by the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) to study a coastal, heavy rainfall event over southwestern Taiwan during 15–16 June 2008. The 36-h cycling run with data assimilation (DA_ALL_DATA run) has a positive impact on the depiction of subsynoptic flow in the model initial conditions at 1200 UTC 15 June, including the warm moist tongue and southwesterly monsoon flow over the open ocean. Furthermore, the cold pool caused by the evaporative cooling of antecedent rains and orographic blocking over southwestern Taiwan are better resolved in the nested high-resolution domain in the DA_ALL_DATA run as compared to the initial conditions provided by the NCEP GFS. As a result, the heavy rainfall along the southwestern coast and afternoon localized heavy rainfall over northern Taiwan are better predicted in the DA_ALL_DATA run. Model sensitivity tests are also performed to diagnose the effects of terrain and rain-evaporative cooling on the intensity and depth of the cold pool and degree of orographic blocking on the southwesterly flow over southwestern Taiwan. It is apparent that including rain-evaporative cooling from antecedent rains and orographic effects in the model initial conditions are important to account for the predicted rainfall distribution of this coastal rainfall event.

An Early Assessment of Medium Range Monsoon Precipitation Forecasts from the Latest High-Resolution NCEP-GFS (T1534) Model over South Asia

Reliable prediction of the South Asian monsoon rainfall and its variability is crucial for various hydrological applications and early warning systems. The National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) is one of the popular global deterministic numerical weather prediction models, which is recently upgraded from T574 to T1534. In this paper, medium range monsoon precipitation forecasts from both the T1534 and T574 models are critically evaluated over the South Asia for the peak monsoon months (July and August) of 2015. Although both the versions of GFS model show similar large-scale monsoon rainfall patterns, the dry bias over the northwest India and equatorial Indian Ocean is noticeably improved in day-1 through day-5 forecasts in the new high-resolution T1534 model. The error decomposition analysis shows similar error characteristics in the monsoon rainfall prediction from both the versions of GFS model, in general. However, forecast improvement factor shows 10–30 % improvement in precipitation forecast from the latest T1534 model over most parts of the South Asia. These preliminary analyses suggest that a suitable bias-correction to the GFS model precipitation forecasts will be useful for any specific application.

Precipitation Calibration Based on the Frequency-Matching Method

Abstract A postprocessing technique is employed to correct model bias for precipitation fields in real time based on a comparison of the frequency distributions of observed and forecast precipitation amounts. Essentially, a calibration is made by defining an adjustment to the forecast value in such a way that the adjusted cumulative forecast distribution over a moving time window dynamically matches the corresponding observed distribution accumulated over a domain of interest, for example, the entire conterminous United States (CONUS), or different River Forecast Center (RFC) regions in the cases examined herein. In particular, the Kalman filter method is used to catch the flow dependence and bias information. Calibration is done on a pointwise basis for a specified domain. Using this unique technique, the calibration of precipitation forecasts for the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) was implemented in May 2004. To further satisfy various users, a recent upgrade to the May 2004 implementation has been made for higher resolution with better analyses. From this study, it was found that this method has a positive impact on the intensity-dominated errors but has some common limitations with extreme events and dry bias elimination like other precipitation calibration methods. Overall, the frequency-matching algorithm substantially improves NCEP Global Forecast System (GFS) and GEFS systematic precipitation forecast errors (or biases) over a wide range of forecast amounts and produces more realistic precipitation patterns. Moreover, this approach improves the deterministic forecast skills measured by most verification scores through applying this method to GFS and GEFS ensemble means.

Modeling of long range transport pathways for radionuclides to Korea during the Fukushima Dai-ichi nuclear accident and their association with meteorological circulations

The Lagrangian FLEXible PARTicle (FLEXPART) dispersion model and National Centers for Environmental Prediction/Global Forecast System (NCEP/GFS) meteorological data were used to simulate the long range transport pathways of three artificial radionuclides: 131I, 137Cs, and 133Xe, coming into Korean Peninsula during the Fukushima Dai-ichi nuclear accident. Using emission rates of these radionuclides estimated from previous studies, three distinctive transport routes of these radionuclides toward the Korean Peninsula for a period from 10 March to 20 April 2011 were exploited by three spatial scales: 1) intercontinental scale – plume released since mid-March 2011 and transported to the North to arrive Korea on 23 March 2011, 2) global (hemispherical) scale – plume traveling over the whole northern hemisphere passing through the Pacific Ocean/Europe to reach the Korean Peninsula with relatively low concentrations in late March 2011 and, 3) regional scale – plume released on early April 2011 arrived at the Korean Peninsula via southwest sea of Japan influenced directly by veering mesoscale wind circulations. Our identification of these transport routes at three different scales of meteorological circulations suggests the feasibility of a multi-scale approach for more accurate prediction of radionuclide transport in the study area. In light of the fact that the observed arrival/duration time of peaks were explained well by the FLEXPART model coupled with NCEP/GFS input data, our approach can be used meaningfully as a decision support model for radiation emergency situations.

Impact of Different Satellite Wind Lidar Telescope Configurations on NCEP GFS Forecast Skill in Observing System Simulation Experiments

AbstractThe Global Wind Observing Sounder (GWOS) concept, which has been developed as a hypothetical space-based hybrid wind lidar system by NASA in response to the 2007 National Research Council (NRC) decadal survey, is expected to provide global wind profile observations with high vertical resolution, precision, and accuracy when realized. The assimilation of Doppler wind lidar (DWL) observations anticipated from the GWOS is being conducted as a series of observing system simulation experiments (OSSEs) at the Joint Center for Satellite Data Assimilation (JCSDA). A companion paper (Riishøjgaard et al.) describes the simulation of this lidar wind data and evaluates the impact on global numerical weather prediction (NWP) of the baseline GWOS using a four-telescope configuration to provide independent line-of-sight wind speeds, while this paper sets out to assess the NWP impact of GWOS equipped with alternative paired configurations of telescopes. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Global Forecast System (GFS) were used, at a resolution of T382 with 64 layers, as the assimilation system and the forecast model, respectively, in these lidar OSSEs. A set of 45-day assimilation and forecast experiments from 2 July to 15 August 2005 was set up and executed.In this OSSE study, a control simulation utilizing all of the data types assimilated in the operational GSI/GFS system was compared to three OSSE simulations that added lidar wind data from the different configurations of telescopes (one-, two-, and four-look configurations). First, the root-mean-square error (RMSE) of wind analysis is compared against the nature run. A significant reduction of the stratospheric RMSE of wind analyses is found for all latitudes when lidar wind profiles are used in the assimilation system. The forecast impacts of lidar data on the wind and mass forecasts are also presented. In addition, the anomaly correlations (AC) of geopotential height forecasts at 500 hPa were evaluated to compare the control and different GWOS telescope configuration experiments. The results show that the assimilation of lidar data from the GWOS (one, two, or four looks) can improve the NCEP GFS wind and mass field forecasts. The addition of the simulated lidar wind observations leads to a statistically significant increase in AC scores.

A Comparison of the Impacts of Radiosonde and AMSU Radiance Observations in GSI Based 3DEnsVar and 3DVar Data Assimilation Systems for NCEP GFS

The impact of observations can be dependent on many factors in a data assimilation (DA) system including data quality control, preprocessing, skill of the model, and the DA algorithm. The present study focuses on comparing the impacts of observations assimilated by two different DA algorithms. A three-dimensional ensemble-variational (3DEnsVar) hybrid data assimilation system was recently developed based on the Gridpoint Statistical Interpolation (GSI) data assimilation system and was implemented operationally for the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS). One question to address is, how the impacts of observations on GFS forecasts differ when assimilated by the traditional GSI-three dimensional variational (3DVar) and the new 3DEnsVar. Experiments were conducted over a 6-week period during Northern Hemisphere winter season at a reduced resolution. For both the control and data denial experiments, the forecasts produced by 3DEnsVar were more accurate than GSI3DVar experiments. The results suggested that the observations were better and more effectively exploited to increment the background forecast in 3DEnsVar. On the other hand, in GSI3DVar, where the observation will be making mostly local, isotropic increments without proper flow dependent extrapolation is more sensitive to the number and types observations assimilated.