National Centers For Environmental Prediction Research Articles

Improved GODAS reanalysis with MOM5 and impact of altimeter assimilation

The National Centers for Environmental Prediction (NCEP) produces operational reanalysis products based on the Global Ocean Data Assimilation System (GODAS) with the Modular Ocean Model (MOM3/MOM4p0d) as a physical model and 3D-Var as the data assimilation method. The Indian National Center for Ocean Information Services (INCOIS) also produces operational ocean analysis with the same GODAS with MOM4p0d. In this study, GODAS is upgraded with MOM5. The improved reanalysis is compared with respect to the EN4 and Ocean ReAnalysis System 5 (ORAS5) for the subsurface temperature and salinity. The microwave-based satellite-derived sea surface temperature (SST) is employed for the independent evaluation of the reanalysis SST products. We have assimilated observed temperature and salinity profiles from all in situ platforms over the global ocean and also assimilated along-track sea level anomaly from altimeter (Jason1 and Jason2) to produce the improved reanalysis. There is a significant improvement in the SST with biases of temperatures reduced from 1.5 to ~ 0.2 °C as compared to without assimilation. Moreover, a significant improvement is found in the subsurface temperature and salinity fields over the northwest Atlantic and Pacific Ocean, Nino 3.4, and Indian Ocean thermocline ridge regions. The biases in this new, improved reanalysis are even lower than ORAS5 when compared with EN4 analysis. Thermocline depth also shows improvement in terms of better representation and capturing seasonal variability. Altimeter assimilation further reduces Root Mean Square Deviation (RMSD) in SST over the global ocean. We also show the improvement in the new reanalysis with respect to the reanalysis based on MOM4p0d.

Large-Scale Surface Air Temperature Bias in Summer over the CONUS and Its Relationship to Tropical Central Pacific Convection in the UFS Prototype 8

Abstract This study aims to understand the source of surface air temperature bias over the contiguous United States (CONUS) during boreal summer (June–September) in the Unified Forecast System (UFS) coupled model prototype 8 (P8), developed by the National Centers for Environmental Prediction (NCEP) and the National Oceanic and Atmospheric Administration (NOAA). The focus is on the subseasonal variability defined as a weekly average in weeks 2–5 of forecast leads (total 224 cases; 4 weeks × 2 initialization dates × 4 months × 7 years). The large-scale surface air temperature bias pattern is extracted using the empirical orthogonal function (EOF) analysis. The associated principal component describes the variability of bias for each reforecasting week throughout the 2011–17 reforecasting period. The leading EOF of surface air temperature bias exhibits an east–west dipole pattern over the CONUS, explaining 31.6% of the total variability of weekly temperature bias. This bias pattern is strongly related to the upper-level Rossby wave induced by a bias in convection over the central tropical Pacific. Furthermore, the mean bias of background flow in the extratropics degrades the representation of teleconnections from the tropics to the midlatitudes. UFS P8 has weaker zonal wind over the North Pacific with stronger vertical wind shear than the ERA5 reanalysis. The weak zonal wind hampers the Rossby wave’s propagation, while strong vertical shear reduces its amplitude.

The role of the Indian Ocean Dipole in modulating the austral spring ENSO teleconnection to the Southern Hemisphere

Abstract. The combined influence of the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on the extratropical circulation in the Southern Hemisphere (SH) during austral spring is examined. Reanalyses and the large ensemble of National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) model outputs were used to compute composites and linear regressions for relevant variables. The results show that a positive IOD can reinforce the El Niño-induced circulation by merging the Indian Ocean wave train with the Pacific South American (PSA) pattern over the Pacific Ocean. In addition, the results obtained with the CFSv2 model output shows that strong positive IODs can contribute to enhancing the circulation signal of the El Niño anomalies and the Indian Ocean wave train. On the other hand, negative IODs in combination with La Niña do not have that combined circulation response. While there is a moderate intensification of the circulation anomalies associated with La Niña, accompanied by some changes in the location of their main action centers, results vary considerably between linear regression, the observed composites, and model composites. Regarding the influence of the IOD activity (independent of ENSO), reanalysis-based results show that the IOD positive phase has a significant impact over the entire SH, while the negative phase is associated with weaker anomalies and less consistent atmospheric response.

Contribution of radio occultation data to atmospheric river landfall forecasts in the eastern North Pacific

AbstractThis study assesses the impact of Global Navigation Satellite System radio occultation (RO) data assimilation (DA) on low‐predictability atmospheric river (AR) landfall forecasts. The period of study is October–March 2020–2022, focusing on AR events over the eastern North Pacific. Eighteen AR landfall events with large forecast errors in the Global Forecast System of the National Centers for Environmental Prediction are selected. Two numerical experiments are conducted for each AR event. One assimilates RO data using a non‐local excess phase operator to improve its initial condition (labeled as EPH forecast), while the other does not (reference run; labeled as REF forecast). Relative to REF, the root‐mean‐squared errors of integrated water vapor transport (IVT), moisture, and wind are reduced by EPH in the main AR activity areas throughout the whole forecast period, except for zonal winds. EPH significantly reduces the underestimation of strong IVT occurrence rate by 2.6%–4.8% and the underestimation of IVT intensity by 15.7–35.0 kg·m−1·s−1, compared to REF. EPH also substantially improves the precipitable water distribution and AR‐related precipitation over the ocean. Despite these basin‐wide improvements, AR coverage, location, and precipitation over land are not significantly affected by RO DA. Specifically, the main contribution of RO DA to the root‐mean‐squared error reduction of AR forecast is the wind improvement prior to AR landfall and the mid‐level moisture improvement after AR landfall. In summary, this study highlights the potential positive contributions of RO DA to AR forecasts, and explores the mechanisms and spatial and temporal characteristics of the improvements.

Air Temperature Variations Analysis of the Hualian M6.9 Earthquake

This study examines the three-dimensional layered air temperature variations associated with the Hualian M6.9 earthquake, which occurred on 18 September 2022, using data from the National Center for Environmental Prediction (NCEP) and extracting background information for air temperature through tidal forces. Changes in air temperature stratification revealed that near the epicenter, temperature anomalies began on 12 September and peaked on 13 September, with pronounced increases in magnitude and area. These anomalies followed a seismic thermal anomaly pattern, i.e., one with greater amplitude and a wider range near the land surface, decreasing with altitude until they dissipated, while the other exhibited high atmospheric temperature anomalies, i.e., the upper atmospheric warming exceeded that near the land surface, likely due to meteorological factors. Stable weather conditions and a low geomagnetic Kp index and Dst index during the research period supported the reliability of these findings. The deformation field recorded by InSAR reflects crustal deformation directly caused by fault slip and stress accumulation; moreover, the area with significantly higher air temperature coincides with the areas with the most concentrated deformation.

Building high-resolution projections of temperature potential changes using statistical downscaling for the future period 2026-2100 in the Highland region of Yemen – A supportive approach for empowering environmental planning and decision-making

Environmental resources and ecological systems are significantly affected by the steady rise of the global temperature. However, the degree of temperature change at the regional and local levels is uncertain. The uncertainty arises from various factors, but mostly due to the short length of ground data and dependency of local studies on the large-scale and spatially coarse output of Global Climate Models (GCMs). Therefore, the output of GCM cannot be directly used in impact assessment studies at a regional and local level. In this study, the Statistical Down-Scaling Model (SDSM) is employed to investigate the magnitude of temperature changes (Minimum and Maximum Temperature) for the future period 2026-2100. The SDSM builds relationships between large-scale predictors and local climate variables, allowing for finer-resolution projections at a regional level. The study utilized the Climate Hazard Infra-Red Temperature with Station (CHIRTS-daily) to complete daily missing records in more than 90 ground stations. Additionally, predictors of the National Center for Environmental Prediction (NCEP) for the historical period (1961-2010) and the Canadian Earth System Model (CanESM2) for the future period (2026-2100) are employed to calibrate SDSM and to build finer-resolution scenarios under two representative concentration pathways; RCP2.6 and RCP8.5. The methodology additionally involved validating the SDSM performance using observed historical data before applying it to future projections. The findings indicate that both minimum and maximum temperatures (T-min and T-max) will increase, with a more pronounced rise in minimum temperature (T-min). Over the future period (2026-2100), the projected average temperature rise is 1.10°C (T-max) and 1.43°C (T-min) under RCP2.6. For RCP8.5, the projected average increases are 1.56°C and 2.3°C for T-max and T-min, respectively. Overall, the most significant increase is projected to occur in the 2090s (2076-2100) under RCP8.5, particularly in the lowlands and wadis of Al Mahwit and Raymah governorate. In these areas, the minimum temperature (T-min) exhibited an increased absolute value of up to 3.2°C. This high rise in temperatures is expected to result in increased evapotranspiration, prolonged droughts, and possibly breakouts of some plant diseases and pests. This would require effective adaptation measures such as harvesting rainwater and growing short-time and heat-resistance crops. Engaging in field visits and social discussions added depth to the study by introducing various traditional methods and indigenous practices. Valuable resources for future efforts to mitigate the potential impacts of climate change are offered by these insights.

Improvement in the Low Temperature Prediction Skill During Cold Winters Over the Mid–High Latitudes of Eurasia in CFSv2

ABSTRACTRegional cold winters have occurred frequently in Eurasia since the beginning of the 21st century, increasing the interannual variability in winter temperatures and increasing the difficulty of prediction. In this study, we evaluate the performance of Climate Forecast version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP) in predicting winter temperature anomalies over the Northern Hemisphere and find that CFSv2 has significantly lower temperature prediction ability for cold winters in the mid–high latitudes of Eurasia since the 21st century. This is mainly due to the stronger response to global warming and the weaker response to sea ice anomalies in the preceding autumn in CFSv2 than the in reanalysis. Accordingly, two targeted correction methods have been developed to improve the prediction ability, with the first method removing the linear temperature trend of CFSv2 predictions and the second method considering the effects of autumn Arctic Sea ice anomalies via a dynamical–statistical correction approach (DSCA). Both methods can effectively improve the prediction ability of winter temperature anomalies in the mid–high latitudes of Eurasia, especially in cold winters. The anomaly correlation coefficient (ACC) increased from −0.03 to 0.13 before and after the modification by the DSCA, and from −0.12 to 0.25 for cold winters. The DSCA significantly reduced the root mean square error (RMSE) of the CFSv2 predictions by approximately 10%.

Assessing global ensemble systems’ forecasts of tropical cyclone genesis in differing environmental flow regimes in the western North Pacific

The forecast probability of tropical cyclone (TC) genesis in the western North Pacific from 2017 to 2020 was investigated using global ensembles from the Japan Meteorological Agency (JMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. National Centers for Environmental Prediction (NCEP), and the Met Office in the United Kingdom (UKMO). The time of TC genesis was defined as the time the TCs were first recorded in the best-track data (Case 1) and as the time they reached the intensity of a Tropical Storm (Case 2). The results in Case 1 showed that differences between the forecast probability based on each global ensemble were large, even for a 1-day forecast, and that mean probability were from 18 % to 74 %. The forecasts based on the NCEP had a large frequency bias and overpredicted TC genesis events. The results indicated that the representation of genesis events differed greatly between global ensembles. The effectiveness of multiple ensembles was investigated. The results from the threat score and the false alarm ratio indicated that multiple ensembles had skillful forecasts. When the forecast probability was examined for environmental patterns of synoptic low-level flow, the mean 5-day forecast probability was highest for the pattern in the confluence region. The results also showed that the forecast probability was much larger in Case 2 than in Case 1. In all global ensembles, the mean probability with a lead time of up to 1-week was below 10 % for both Case 1 and 2. This result indicates that even with today's operational forecasting systems, it is difficult to regularly predict TC genesis events with a 1-week lead time with high confidence. These results provide a better understanding of TC genesis forecast products in each global ensemble and will be useful information when multiple-ensemble products are created.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Intercomparisons of the Latest Precipitation Estimates from Satellite, Reanalysis, and Merged Products over Alaska

Abstract This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite–gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products.

Simulated Directional Wave Spectra of the Wind Sea and Swell under Typhoon Mangkhut

A third-generation wave model is driven by the synthetic wind field combined with the revised Holland wind and surface wind product from the National Centers for Environmental Prediction (NCEP). The temporal and spatial characteristics of the wind waves and swell during the typhoon are studied, as well as the responses of their wave energy spectra to the source terms. The results show that the typhoon waves have a more complicated asymmetric structure than the wind field, and the maximum significant wave height is always located on the right side of the direction along which the typhoon is moving, where wind waves are dominant, due to the extended fetch. The nonlinear wave–wave interaction helps to redistribute the energy of the wind seas at a high frequency to the remotely generated swells at a low frequency, ensuring that the typhoon wave’s energy spectrum remains unimodal. This process occurs in regions without extended fetch, and a similar continued downshift in frequency as the wave–wave interaction occurs for the wind input as well when the waves outrun the typhoon, due to the nonlinear coupling between the wind and growing swells.

Aero-optical effects of a hypersonic vehicle based on the refractive index step ray tracing method

The aero-optical effect caused by the high-speed flight of aircraft can affect the accuracy of astronomical navigation, so studying the laser transmission characteristics in high-speed flow fields is essential. This study adopts a 3D spherical blunt cone model as the physical aircraft model, incorporates the latest reanalysis data from the National Centers for Environmental Prediction as atmospheric parameters, and uses Fluent software to establish a hypersonic aircraft external flow field model. The ray tracing error is calculated using a method based on the refractive index step size. Results indicate that, under the conditions of flying altitude of 5 km and incident angle of 45°, the proposed ray tracing method has better accuracy than the traditional method. Compared with the Runge–Kutta method and the method based on geometric step size, the proposed ray tracing method reduces the error by 46.4% and 12.5%, respectively, at the flight Mach number of 5.

Assessments of multiple metocean forecasts in the North Atlantic Ocean

Accurate wind and wave forecasts are crucial for a variety of marine operations, including ship design, navigation safety, and engineering. This study evaluates the predictability of wind and wave conditions in the North Atlantic Ocean using both deterministic and ensemble forecast models. A dataset of 29 deep water buoys was used to validate deterministic and ensemble forecasts from the National Centres for Environmental Prediction (NCEP), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the German Weather Service (DWD). A comparison of deterministic and ensemble forecast performance was conducted using eight statistical metrics focusing on the significant wave heights associated with 10-m wind speed. The results indicate that ensemble models exhibit higher correlation coefficients and lower scatter root mean square error especially beyond the 7th forecast day, outperforming deterministic models. The findings suggest that ensemble forecasts offer a more reliable estimation of forecast, enhancing predictability for marine operations.

Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin.

Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Radiance Data in GFS

Abstract Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of “anchor” observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes. Significance Statement This study aims to evaluate the impact of aircraft reconnaissance dropsondes on the assimilation of satellite radiance data, utilizing observations from the 2020 Atmospheric River Reconnaissance program. The key findings reveal a substantial enhancement in the model first guess and improved estimates of radiance biases. Notably, there is a significant 5%–10% increase in microwave radiance observations over the northeastern Pacific and North America, with positive yet modest effects observed in tropospheric infrared radiance. These findings underscore the crucial role of atmospheric river reconnaissance dropsondes as anchor data, enhancing the assimilation of radiance observations. In essence, the inclusion of these dropsondes in routine networks is particularly valuable for optimizing data assimilation in regions with sparse observational data.

Sensitivity analysis of cumulus parameterization scheme and data sources to simulate thunderstorms over Bangladesh using WRF model

To optimize weather research and forecasting (WRF) model, a sensitivity analysis has been performed for test thunderstorm events to determine the best combination of cumulus physics (CU) and data source options to be used for simulating thunderstorms over Bangladesh. Six combinations for three cumulus physics options namely, Kain-Fritsch Scheme (KF) (CU-1), Betts-Miller-Janjic Scheme (BMJ) (CU-2) and Grell-Dévényi (GD) Ensemble Scheme (CU-3) and two different data sources namely, National Center for Environmental Prediction (NCEP) Final Analysis (FNL) and the Global Forecast System (GFS) data have been used for this purpose. WRF model has been run on a single domain of 9 km horizontal resolution utilizing the combinations of these cumulus physics and six hourly GFS/FNL datasets for 48 h; from 0000 UTC of 14 May 2017 to 0000 UTC of 16 May 2017 and from 0000 UTC of 29 March 2018 to 0000 UTC of 31 March 2018 as initial and lateral boundary conditions for test event-1 and test event-2 respectively. Model simulated output for rainfall, relative humidity; mean sea level pressure and temperature have been compared with the observed data of Bangladesh Meteorological Department (BMD). Then absolute error of each simulated parameter has been calculated. On the basis of root mean square error (RMSE) for each parameter, better cumulus physics and data source combination has been selected. It is found that, CU-1 and GFS dataset combination exhibits less RMS error in 4 sections out of 12 sections for event-1 and in 5 sections out of 12 sections for event-2. So, it may be expected that Cu_1 and GFS dataset combination may be used for simulating thunderstorm over Bangladesh. After finalizing model physics options and data sets, another test case of TS has been simulated and different parameters are analyzed for justification of WRF setup. It is found that WRF model captured MSLP, temperature and relative humidity very well. But in case of rainfall the simulated value is much smaller than the observed value. For more accuracy more case studies may be carried out including for fair weather days as well as severe TS days.

Impact of Model Resolution and Initial/Boundary Conditions in Forecasting Low-Level Atmospheric Fields over the Incheon International Airport

Abstract This study investigates the impact of initial conditions/boundary conditions (ICs/BCs) and horizontal resolutions on forecast for average weather conditions, focusing on low-level weather variables such as 2-m temperature (T2m), 2-m water vapor mixing ratio (Q2m), and 10-m wind speed (WS10). A Weather Research and Forecasting (WRF) Model is used for regional mesoscale model simulations and large-eddy simulations (LESs). The 6-h-interval forecast fields generated by the Global Forecast System of the National Centers for Environmental Prediction and the Korean Integrated Model of the Korea Meteorological Administration are utilized as ICs/BCs for the regional models. Numerical experiments are performed for 24 h starting at 0000 UTC on each day in April 2021 when the average monthly wind speed was strongest during 10 years (2011–20). A comparison of model simulations with observations obtained around the Yeongjong Island, where Incheon International Airport is situated, shows that the regional models capture the time series of T2m, Q2m, and WS10 more effectively than the global model forecasts. Moreover, the LES experiments with a 100-m horizontal grid spacing simulate higher Q2m and lower WS10 during the daytime compared to the 1-km WRF. This results in a deterioration of their time-series correlation with the observations. Meanwhile, the 100-m LES forecasts time series of T2m over ocean stations and Q2m over land stations, as well as probability density functions of low-level weather variables, more accurately than that of the 1-km WRF. Our study also emphasizes the need for caution when comparing high-resolution model results with observation values at specific stations due to the high spatial variability in low-level meteorological fields.

Deciphering Mean Flow–Eddy Interaction in Pacific–North American Teleconnection Linked to Storm Tracks at Subseasonal Time Scales

Abstract The features of large-scale atmospheric circulations, storm tracks, and the mean flow–eddy interaction during winter Pacific–North American (PNA) events are investigated using National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data at subseasonal time scales from 1979 to 2022. The day-to-day variations of storm-track activity and streamfunction reveal that storm-track activity varies along the evolution of mean flow. To better understand storm-track variability with the mean flow–eddy interaction, further exploration is made by analyzing local energy energetics. The changes in horizontal and vertical baroclinic energy conversions from background flow correspond to the storm-track anomalies over the North Pacific, indicating that the anomalies in storm tracks are due to the anomalous mean flow associated with PNA patterns impacting energy conversion through mean flow–eddy interaction. Eddy feedback driven by vorticity and heat fluxes is analyzed. This provides a concrete illustration of how eddy feedback serves as a positive factor for the upper-tropospheric circulation anomalies associated with the PNA pattern. Significance Statement The background flow plays a crucial role in governing storm-track dynamics. Our emphasis is on the Pacific storm tracks (PST) and their relation to Pacific–North American (PNA) patterns at subseasonal time scales. We unveil the relationship between anomalies of PST and PNA patterns using local energetics and eddy feedback on a day-by-day basis. It is noteworthy that the evolution of anomalous storm tracks during PNA events is the manifestation of mean flow–eddy interaction. Additionally, we provide detailed confirmation of the impact of anomalous storm tracks on large-scale anomalies associated with the PNA pattern.

Hydrochemical and isotopic characteristics on the southern and northern slopes of the Himalayas: spatio-temporal controls and source apportionment

Water-soluble ions, inorganic nitrogen, and stable isotopes in precipitation were assessed from the southern (Koshi Tappu and Khandbari) and northern slopes (Lhasa and SET) of the Himalayas to understand the sources, chemistry of regional precipitation, and climatic processes. Water soluble ions showed distinct seasonal variation, with higher concentrations in the non-monsoon. The concentration of ionic species was highest in Koshi Tappu, followed by Lhasa, SET, and Khandbari. The sources were from the terrigenous (Ca2+, HCO3−), marine (Na+ and Cl−), anthropogenic (SO42−, NO3−, and NH4+), terrigenous and marine (Mg2+), and biomass-burning (K+). The southern slope, relative to the northern, was more prone to anthropogenic emissions with higher deposition. Among all sites, inorganic nitrogen deposition at Koshi Tappu was higher than the threshold value (10 kg ha−1 y−1). The isotopic composition during the study period was higher in non-monsoon, started declining from June, and depleted in July and August compared to other months, i.e., the monsoon mature phase, along the south-to-north transect. The diminished value of stable isotopes in precipitation with increasing altitude underlines the evidence of the orographic effect in isotopic composition. Our study delineated that the higher/lower d-excess value increased with altitude on the southern/northern slope of the Himalayas. The backward trajectory analysis and the National Centers for Environmental Prediction's Final (NCEP FNL) datasets identified that most of the trajectories arrived from warm and humid low-latitude regions during monsoon and westerlies in non-monsoon. Thus, the chemical characteristics and stable isotopic composition of precipitation differed on the southern and northern slopes of the Himalayas by orographic effect and various sources. This study provides new insights into the atmospheric environment and climatic control of stable isotopes in the Himalayan Tibetan Plateau and facilitates monitoring of transboundary air pollution.

Ocean component of the first operational version of Hurricane Analysis and Forecast System: Evaluation of HYbrid Coordinate Ocean Model and hurricane feedback forecasts

The first operational version of the coupled Hurricane Analysis and Forecast System (HAFSv1) launched in 2023 consists of the HYbrid Coordinate Ocean Model (HYCOM) and finite-volume cubed-sphere (FV3) dynamic atmosphere model. This system is a product of efforts involving improvements and updates over a 4-year period (2019–2022) through extensive collaborations between the Environmental Modeling Center at the US National Centers for Environmental Prediction (NCEP) and NOAA Atlantic Oceanography and Meteorology Laboratory. To provide two sets of numerical guidance, the initial operational capability of HAFSv1 was configured to two systems—HFSA and HFSB. In this study, we present in-depth analysis of the forecast skills of the upper ocean that was co-evolved by the HFSA and HFSB. We chose hurricane Laura (2020) as an example to demonstrate the interactions between the storm and oceanic mesoscale features. Comparisons performed with the available in situ observations from gliders as well as Argos and National Data Buoy Center moorings show that the HYCOM simulations have better agreement for weak winds than high winds (greater than Category 2). The skill metrics indicate that the model sea-surface temperature (SST) and mixed layer depth (MLD) have a relatively low correlation. The SST, MLD, mixed layer temperature (MLT), and ocean heat content (OHC) are negatively biased. For high winds, SST and MLT are more negative, while MLD is closer to the observations with improvements of about 8%–19%. The OHC discrepancy is proportional to predicted wind intensity. Contrarily, the mixed layer salinity (MLS) uncertainties are smaller and positive for higher winds, probably owing to the higher MLD. The less-negative bias of MLD for high winds implies that the wind-force mixing is less effective owing to the higher MLD and high buoyancy stability (approx. 1.5–1.7 times) than the observations. The heat budget analysis suggests that the maximum heat loss by hurricane Laura was O(< 3°C per day). The main contributor here is advection, followed by entrainment, which act against or with each other depending on the storm quadrant. We also found relatively large unaccountable heat residuals for the in-storm period, and the residuals notably led the heat tendency, meaning that further improvements of the subscale simulations are warranted. In summary, HYCOM simulations showed no systematic differences forced by either HFSA or HFSB.