Statistical Interpolation Research Articles

Impacts of Radio Occultation Data on Typhoon Forecasts as Explored by the Global MPAS-GSI System

Global Navigation Satellite System (GNSS) radio occultation (RO) provides plentiful sounding profiles over regions lacking conventional observations. The Gridpoint Statistical Interpolation (GSI) hybrid system for assimilating RO data is integrated in this study with the Model for Prediction Across Scales–Atmosphere (MPAS) to improve tropical cyclone forecasts. After the MPAS-GSI assimilation cycles, dynamical vortex initialization (DVI) that may effectively spin up the initial inner typhoon vortex through cycled model integration is implemented to improve the initial analysis fit to the best track position as well as maximum wind or pressure intensity for Typhoon Nepartak (2016) that moved northwestward toward southern Taiwan. During the cycling assimilation, assimilation with RO data improves the temperature and moisture analysis, and largely reduces the forecast errors compared to those without RO data assimilation. The two RO operators that assimilate local bending angle or refractivity produce similar analyses, but the temperature and moisture increments from bending angle assimilation are slightly larger than those from refractivity assimilation. The MPAS forecasts at 60-15 km resolution show that the typhoon track prediction is improved with RO data, especially using bending angle data. The reduction in track deviations is explained by the wavenumber-one potential vorticity budget for several forecasts associated with the track deflection near southern Taiwan. Assimilation of RO data has fewer impacts on the typhoon intensity forecast compared to the DVI that largely improves the initial and thus forecasted intensity of the typhoon but at the cost of a slightly degraded track. Use of the enhanced 3 km resolution in the typhoon path also further improved the forecasts with and without the DVI. The feasible performance of the MPAS-GSI system with the RO data impact is also illustrated for Typhoon Mitag (2019), that passed around northern Taiwan.

Effects of Direct Assimilation of FY-4A AGRI Water Vapor Channels on the Meiyu Heavy-Rainfall Quantitative Precipitation Forecasts

In this study, the regional Weather Research and Forecasting model (WRF)-based quantitative precipitation forecasts (QPFs) are conducted for an extreme Meiyu rainfall event over East Asia in 2020. The data of water vapor channels 9 and 10 from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite are assimilated through the Gridpoint Statistical Interpolation (GSI) system. It shows that a reasonable amount of assimilated AGRI data can produce reasonable water vapor increments, compared to the too sparse or dense assimilated AGRI observations. In addition, the critical success indexes (CSIs) of the precipitation forecasts within 72 h are obviously improved. The enhanced variational bias correction (VarBC) scheme is applied to remove the air-mass and scan-angle biases, and the mean observation-minus-background (O − B) values before and after the VarBC of channel 9 are −1.185 and 0.02 K, respectively, and those of channel 10 are −0.559 and −0.01 K, respectively. Assimilating the upper-level channel 9 data of AGRI (EXP_WV9) lead to a neutral-to-positive effect on QPFs, compared to the control run (CTL), which is based on the assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) data. In particular, the CSIs from 42 to 72 h are significantly improved. However, the assimilation of the AGRI channel 10 (EXP_WV10) shows a neutral-to-negative effect on QPFs in this study, probably due to the complicated surface situations. This study confirms the feasibility of assimilating the water vapor channel data of FY4A AGRI in the GSI system and highlights the importance of assimilating AGRI channel 9 data to improve the QPFs of the Meiyu rainfall event.

Assimilation of GPM-retrieved ocean surface meteorology data for two snowstorm events during ICE-POP 2018

Abstract. As a component of the National Aeronautics and Space Administration's (NASA's) Weather Focus Area and Global Precipitation Measurement (GPM) Ground Validation participation in the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field research and forecast demonstration programs, hourly ocean surface meteorology properties were retrieved from the GPM microwave observations for January–March 2018. In this study, the retrieved ocean surface meteorological products – 2 m temperature, 2 m specific humidity, and 10 m wind speed – were assimilated into a regional numerical weather prediction (NWP) framework. This explored the application of these observations for two heavy snowfall events during the ICE-POP 2018, on 27–28 February and 7–8 March 2018. The Weather Research and Forecasting (WRF) model and the community Gridpoint Statistical Interpolation (GSI) were used to conduct high-resolution simulations and data assimilation experiments. The results indicate that the data assimilation has a large influence on surface thermodynamic and wind fields in the model initial condition for both events. With cycled data assimilation, a significantly positive influence of the retrieved surface observation was found for the March case, with improved quantitative precipitation forecasts and reduced errors in temperature forecasts. A slightly smaller yet positive impact was also found in the forecast for the February case.

Data Assimilation of Doppler Wind Lidar for the Extreme Rainfall Event Prediction over Northern Taiwan: A Case Study

On 4 June 2021, short-duration extreme precipitation occurred in Taipei. Within 2 h, over 200 mm of rainfall accumulated in the Xinyi district. In this study, advanced data assimilation technology (e.g., hybrid data and 3D variations) was incorporated to develop a high-resolution, small-scale (e.g., northern Taiwan) data assimilation forecast system, namely the weather research and forecast-grid statistical interpolation (WRF-GSI) model. The 3D wind field data recorded by the Doppler wind lidar system of Taipei Songshan Airport were assimilated for effective simulation of the extreme precipitation. The results revealed that the extreme rainfall was caused by the interaction between the northeast wind incurred by a front to the north of Taiwan, a humid southerly wind generated by Typhoon Choi-wan, and the regional sea–land breeze circulation. For the Xinyi district, the WRF-GSI_lidar model reported accumulated rainfall 30 mm higher than that in the non-assimilated experiment (WRF-GSI_noDA), indicating that the WRF-GSI model with lidar observation was improved 15% more than the nonassimilated run.

Data assimilation of adaptive observation and application for typhoon forecasts over the Western North Pacific

In this study, we added the post-processing module of the Ensemble Transform Sensitivity (ETS) to identify regions sensitive to adaptive observations of different variables at different levels for typhoon forecasts. We selected five cases between 2015 and 2020 over the Western North Pacific (WNP) using 50 ensemble forecasts from the European Center provided by the THORPEX Interactive Grand Global Ensemble (TIGGE) portal. Furthermore, intercomparison experiments were performed to explore the impact of adaptive observational data assimilation with conventional observations and Himawari-8 data over the sensitive (SEN), non-sensitive (NOSEN), and all studied regions (ALL) on typhoon forecasts using the Gridpoint Statistical Interpolation (GSI) assimilation system of the National Centers for Environmental Prediction (NCEP) of United States. The results show that the assimilation of less observational data in the SEN areas can achieve the forecast effect of assimilation in ALL regions, the intensity error can be reduced by approximately 15%, and the track error is reduced in some cases. Finally, detailed analysis of the circulation field and flux perspectives for Typhoon Lekima, which occurred in August 2019 over the WNP, confirmed our conclusion. At 1800 UTC on August 8, the data assimilation was performed over the SEN, NOSEN, and ALL areas. The initial circulation field, in which the vorticity increased in the southwest of the typhoon and decreased in the southeast, benefited the westward movement of the typhoon. The 18 h forecast indicated that the circulation in the SEN area assimilation experiment showed a negative vorticity increase in the southwest side of the typhoon and positive potential temperature as well as water vapor forecast differences on its north side, which contrasted with the results obtained in the NOSEN and ALL experiments. This provided favorable weather conditions for the typhoon to change direction and move northward. Targeted observation experiments investigating Typhoon Lekima indicated that assimilating observations of sensitive areas greatly improves typhoon forecasting and assimilation efficiency. However, the ALL experiment yielded the best forecast among the three cases.

Investigation of the Potential Saturation of Information from Global Navigation Satellite System Radio Occultation Observations with an Observing System Simulation Experiment

Abstract The potential impact of large numbers of Global Navigation Satellite System radio occultation (GNSS-RO) observations on numerical weather prediction is investigated using a global observing system simulation experiment (OSSE). The hybrid four-dimensional ensemble variational Gridpoint Statistical Interpolation (GSI) data assimilation system and Global Earth Observing System (GEOS) model are used to ingest up to 100 000 GNSS-RO soundings per day in addition to the current suite of conventional and radiance data. Analysis quality, forecast skill, and forecast sensitivity to observation impact are examined with differing quantities of additional GNSS-RO profiles. It is found that saturation of information from additional RO soundings has not been reached with 100 000 soundings per day. There are some indications of suboptimal performance of the GSI in handling GNSS-RO observations particularly in the middle- and lower-tropospheric extratropics.

Combined Assimilation of Doppler Wind Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved Hurricane Prediction with the NCEP Regional HWRF System

Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement.

An Observing System Simulation Experiment (OSSE) to Study the Impact of Ocean Surface Observation from the Micro Unmanned Robot Observation Network (MURON) on Tropical Cyclone Forecast

The Micro Unmanned Robot Observation Network (MURON) is a planned in-situ observation network over the surface of West Pacific Ocean, and it is designed to sample high spatial and temporal resolution observations of wind and mass fields over the ocean surface. The impacts of MURON observations for Tropical Cyclone (TC) intensity forecast are investigated using Observation System Simulation Experiments. The regional Ensemble Kalman Filter (EnKF) system of Gridpoint Statistical Interpolation is used with the Advanced Research version of the Weather Research and Forecasting model to conduct OSSEs for typhoon Haiyan (2013) while Haiyan goes through rapid intensification. Assimilating MURON observations improves the TC structure and intensity analysis and forecast. The intensity forecast is improved largely due to the correction of initial vorticity and vertical transport of mass flux. The improvement of intensity forecast is attributed largely to the assimilated MURON wind observations when Haiyan is at the tropical disturbance stage, and then by the MURON mass observations when Haiyan enters the tropical storm stage. In addition, our results show that the quality of moisture analysis is sensitive to the choice of the moisture control variable (CV) in the EnKF system. Using the default pseudo relative humidity (PRH) as the moisture CV degrades the accuracy of the moisture analysis. This is likely due to the neglect of updated temperature field during the nonlinear conversion from the PRH CV to the mixing ratio variable and due to the larger deviation of the PRH from Gaussian distribution. The use of mixing ratio moisture CV mitigates these problems.

Impacts of Assimilating CYGNSS Satellite Ocean-Surface Wind on Prediction of Landfalling Hurricanes with the HWRF Model

This study examines the impacts of assimilating ocean-surface winds derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS) on improving the short-range numerical simulations and forecasts of landfalling hurricanes using the NCEP operational Hurricane Weather Research and Forecasting (HWRF) model. A series of data assimilation experiments are performed using HWRF and a Gridpoint Statistical Interpolation (GSI)-based hybrid 3-dimensional ensemble-variational (3DEnVar) data assimilation system. The influence of CYGNSS data on hurricane forecasts is compared with that of Advanced Scatterometer (ASCAT) wind products that have already been assimilated into the HWRF forecast system in a series of assimilation experiments. The effects of different versions of CYGNSS data (V2.1 vs. V3.0) on hurricane forecasts are evaluated. The results indicate that CYGNSS ocean-surface wind can lead to improved numerical simulations and forecasts of hurricane track and intensity, asymmetric wind structure, and precipitation. The impacts of CYGNSS on hurricane forecasts are comparable and complementary to the operational use of ASCAT satellite data products. The dependence of the relative impacts of different versions of CYGNSS data on optimal thinning distances is evident.

Use of Power Transform Total Number Concentration as Control Variable for Direct Assimilation of Radar Reflectivity in GSI En3DVar and Tests with Six Convective Storms Cases

Abstract When using a double-moment microphysics scheme, both hydrometeor mixing ratios and number concentrations are part of the state variables that are needed to initialize convective-scale forecasting. In the Thompson microphysics scheme, both mixing ratio and total number concentration of rainwater (Ntr) are predicted and they are also involved in the reflectivity observation operator. In such a case, when directly assimilating reflectivity using Ntr as the control variable (denoted as CVnr) within a variational framework, the large dynamic range of Ntr and the nonlinear relationship between reflectivity and Ntr prevent efficient minimization convergence. Using logarithmic Ntr as the control variable (CVlognr) alleviates the problem to some extent but can produce spurious analysis increments in Ntr. In this study, a general power transform of Ntr is proposed as the new control variable for Ntr (CVpnr) where the nonlinearity of transform can be adjusted by tuning the exponent parameter. This formulation is implemented within the Gridpoint Statistical Interpolation ensemble-3DVar system. The performance of CVpnr with an optimal exponent parameter value of 0.4 is compared with those of CVnr and CVlognr for the analysis and prediction of a supercell case of 16 May 2017 in more detail. CVpnr with optimal exponent yields faster convergence of cost function minimization than CVnr. Subjective and objective evaluations of analyzed and predicted reflectivity and hourly precipitation indicate that the optimized CVpnr outperforms the other two methods. When applied to five additional cases from May 2017, overall statistics show that CVpnr produces generally superior forecasts and is therefore the preferred choice.

Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms

Surface ocean dynamics play a key role in the Earth system, contributing to regulate its climate and affecting the marine ecosystem functioning. Dynamical processes occur and interact in the upper ocean at multiple scales, down to, or even less than, few kilometres. These scales are not adequately resolved by present observing systems, and, in the last decades, global monitoring of surface currents has been based on the application of geostrophic balance to absolute dynamic topography maps obtained through the statistical interpolation of along-track satellite altimeter data. Due to the cross-track distance and repetitiveness of satellite acquisitions, the effective resolution of interpolated data is limited to several tens of kilometres. At the kilometre scale, sea surface temperature pattern evolution is dominated by advection, providing indirect information on upper ocean currents. Computer vision techniques are perfect candidates to infer this dynamical information from the combination of altimeter data, surface temperature images and observing-system geometry. Here, we exploit one class of image processing techniques, super-resolution, to develop an original neural-network architecture specifically designed to improve absolute dynamic topography reconstruction. Our model is first trained on synthetic observations built from a numerical general-circulation model and then tested on real satellite products. Provided concurrent clear-sky thermal observations are available, it proves able to compensate for altimeter sampling/interpolation limitations by learning from primitive equation data. The algorithm can be adapted to learn directly from future surface topography, and eventual surface currents, high-resolution satellite observations.

The Aerosol Module in the Community Radiative Transfer Model (v2.2 and v2.3): accounting for aerosol transmittance effects on the radiance observation operator

Abstract. The Community Radiative Transfer Model (CRTM), a sensor-based radiative transfer model, has been used within the Gridpoint Statistical Interpolation (GSI) system for directly assimilating radiances from infrared and microwave sensors. We conducted numerical experiments to illustrate how including aerosol radiative effects in CRTM calculations changes the GSI analysis. Compared to the default aerosol-blind calculations, the aerosol influences reduced simulated brightness temperature (BT) in thermal window channels, particularly over dust-dominant regions. A case study is presented, which illustrates how failing to correct for aerosol transmittance effects leads to errors in meteorological analyses that assimilate radiances from satellite infrared sensors. In particular, the case study shows that assimilating aerosol-affected BTs significantly affects analyzed temperatures in the lower atmosphere across several regions of the globe. Consequently, a fully cycled aerosol-aware experiment improves 1–5 d forecasts of wind, temperature, and geopotential height in the tropical troposphere and Northern Hemisphere stratosphere. Whilst both GSI and CRTM are well documented with online user guides, tutorials, and code repositories, this article is intended to provide a joined-up documentation for aerosol absorption and scattering calculations in the CRTM and GSI. It also provides guidance for prospective users of the CRTM aerosol option and GSI aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are briefly discussed.

High-Resolution Gridded Air Temperature Data for the Urban Environment: The Milan Data Set

Temperature is the most used meteorological variable for a large number of applications in urban resilience planning, but direct measurements using traditional sensors are not affordable at the usually required spatial density. On the other hand, spaceborne remote sensing provides surface temperatures at medium to high spatial resolutions, almost compatible with the needed requirements. However, in this case, limitations are represented by cloud conditions and passing times together with the fact that surface temperature is not directly comparable to air temperature. Various methodologies are possible to take benefits from both measurements and analysis methods, such as direct assimilation in numerical models, multivariate analysis, or statistical interpolation. High-resolution thermal fields in the urban environment are also obtained by numerical modelling. Several codes have been developed to resolve at some level or to parameterize the complex urban boundary layer and are used for research and applications. Downscaling techniques from global or regional models offer another possibility. In the Milan metropolitan area, given the availability of both a high-quality urban meteorological network and spaceborne land surface temperatures, and also modelling and downscaling products, these methods can be directly compared. In this paper, the comparison is performed using: the ClimaMi Project high-quality data set with the accurately selected measurements in the Milan urban canopy layer, interpolated by a cokriging technique with remote-sensed land surface temperatures to enhance spatial resolution; the UrbClim downscaled data from the reanalysis data set ERA5; a set of near-surface temperatures produced by some WRF outputs with the building environment parameterization urban scheme. The comparison with UrbClim and WRF of the cokriging interpolated data set, mainly based on the urban canopy layer measurements and covering several years, is presented and discussed in this article. This comparison emphasizes the primary relevance of surface urban measurements and highlights discrepancies with the urban modelling data sets.

An SLAM Algorithm Based on Laser Radar and Vision Fusion with Loop Detection Optimization

SLAM (Simultaneous Localization And Mapping, SLAM) is the key technology for mobile robots to obtain information about their position and surrounding environment, but both laser radar and vision sensors have limitations in solving SLAM problems, for example: a single laser sensor can’t do mapping effectively for hollowed-out obstacles, while a single vision sensor can’t work well in areas without texture. Besides, the existing loop detection technology still has some problems such as large calculation resources and errors. To solve the above problems, an improved SLAM algorithm laser and vision fusion was presented in this paper. In this algorithm, a Gaussian statistical linear interpolation algorithm was used to interpolate the laser point cloud, and then the interpolated laser raster map and visual raster map are fused by the Bayesian method to obtain a denser and more accurate map. Furthermore, the histogram was used to improve the loop detection part to further improve the positioning accuracy. The improved loop detection algorithm was simulated and verified by running the TUM data set. On this basis, a mobile robot experiment system was built to do mapping in the laboratory scene. The experimental results show that the algorithm proposed in this paper can map the hollowed-out area accurately and effectively, it plays a significant role in improving the drawing effect of the robot, and can provide more accurate location information, which has good practicability.

The Influence of Aerosols on Satellite Infrared Radiance Simulations and Jacobians: Numerical Experiments of CRTM and GSI

For a variational data assimilation (DA) system that assimilates radiance observations, the simulated brightness temperature (BT) at the top of the atmosphere and the corresponding Jacobians carried out by the radiance observation operator are needed information. Previous studies reported that the incorporation of aerosol information into the radiance observation operator leads to cooler simulated infrared (IR) BTs and warmer temperature analyses at low levels of the atmosphere. However, the role of the aerosol-affected Jacobians in the DA system, which not only affect the determination of analysis increments but also influence the quality control and the bias correction algorithm, is yet to be investigated. This study examines the aerosol impacts on the sensitivity of IR radiance simulations, Jacobians, and the analysis increments by conducting two experiments: (i) sensitivity tests to assess how the different aspects of the aerosol profiles (i.e., mass loading, peak aerosol level, aerosol thickness layer, and bin partition) affect the simulated BT and the Jacobians from the Community Radiative Transfer Model (CRTM), which is the radiance observation operator in the Gridpoint Statistical Interpolation (GSI) analysis system; (ii) single IR observation experiments using GSI to investigate how the aerosol-affected atmospheric Jacobians influence the analysis increment. The results show that dust aerosols produce the strongest cooling to simulated BTs under similar aerosol optical depths; simulated BTs and Jacobians are most sensitive to the loading and peak altitude of the aerosol layer; simulated BTs become more sensitive to the temperature of the aerosol layer; aerosol-induced differences in atmospheric Jacobians lead to considerable changes to temperature and moisture increments. These results provide a better understanding of the aerosol impacts on each component involved in radiance DA, which can provide guidance for assimilating aerosol-affected IR observations.

A Novel Classification Method with Cubic Spline Interpolation

Classification is the last, and usually the most time-consuming step in recognition. Most recently proposed classification algorithms have adopted machine learning (ML) as the main classification approach, regardless of time consumption. This study proposes a statistical feature classification cubic spline interpolation (FC-CSI) algorithm to classify emotions in speech using a curve fitting technique. FC-CSI is utilized in a speech emotion recognition system (SERS). The idea is to sketch the cubic spline interpolation (CSI) for each audio file in a dataset and the mean cubic spline interpolations (MCSIs) representing each emotion in the dataset. CSI interpolation is generated by connecting the features extracted from each file in the feature extraction phase. The MCSI is generated by connecting the mean features of 70% of the files of each emotion in the dataset. Points on the CSI are considered the new generated features. To classify each audio file according to emotion, the Euclidian distance (ED) is found between each CSI and all MCSIs of all emotions in the dataset. Each audio file is classified according to the nearest MCSI to the CSI representing it. The three datasets used in this work are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Berlin (Emo-DB), and Surrey Audio-Visual Expressed Emotion (SAVEE). The proposed work shows fast classification and high accuracy of results. The classification accuracy, i.e., the proportion of samples assigned to the correct class, using FC-CSI without feature selection (FS), was 69.08%, 92.52%, and 89.1% with RAVDESS, Emo-DB, and SAVEE, respectively. The results of the proposed method were compared to those of a designed neural network called SER-NN. Comparisons were made with and without FS. FC-CSI outperformed SER-NN on Emo-DB and SAVEE, and underperformed on RAVDESS, without using an FS algorithm. It was noticed from experiments that FC-CSI operated faster than the same system utilizing SER-NN.

Assimilating C-Band Radar Data for High-Resolution Simulations of Precipitation: Case Studies over Western Sumatra

Accurate high-resolution precipitation forecasts are critical yet challenging for weather prediction under complex topography or severe synoptic forcing. Data fusion and assimilation aimed at improving model forecasts, as one possible approach, has gained increasing attention in past decades. This study investigates the influence of the observations from a C-band Doppler radar over the west coast of Sumatra on high-resolution numerical simulations of precipitation around its vicinity under the Madden–Julian oscillation (MJO) in January and February 2018. Cases during various MJO phases were selected for simulations with an advanced research version of the weather research and forecasting (WRF) model at a cloud-permitting scale (~3 km). A 3-dimensional variational (3DVAR) data assimilation method and a hybrid three-dimensional ensemble–variational data assimilation (3DEnVAR) method, based on the NCEP Gridpoint Statistical Interpolation (GSI) assimilation system, were used to assimilate the radar reflectivity and the radial velocity data. The WRF-simulated precipitation was validated with the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data, and the fractions skill score (FSS) was calculated in order to evaluate the radar data impacts objectively. The results show improvements in the simulated precipitation with hourly radar data assimilation 6 h prior to the simulations. The modifications with assimilation were validated through the observation departure and moist convection. It was found that forecast improvements are relatively significant when precipitation is more related to local-scale convection but rather small when the background westerly wind is strong under the MJO active phase. The additional simulation experiments, under a 1- or 2-day assimilation cycle, indicate better improvements in the precipitation simulation with 3DEnVAR radar assimilation than those with the 3DVAR method.

Determination of actual characteristic curve of main ventilation fan at Quang Ninh underground coal mines using field measurement method

Determining a proper operation mode of the main ventilation fan at an underground coal mine primarily uses the theoretical characteristic curves of the fan’s manufacturer. Because these curves are developed in laboratory-standard conditions, the characteristic curves under different conditions in practice significantly change, seriously impacting the ventilation efficiency and environmental safety of mine. This paper presents a determination of the main fan's actual characteristic curve using a field measurement method. The method involves the (i) simultaneous measurement of airflow and air pressure at designated locations in fan drift and ventilation crosscut and (ii) statistical analysis and interpolation of the measured data. The results show that the fan actual pressure curve is permanently displaced to the left and steeper than the corresponding theoretical pressure curve in an on-site operating mode. The finding points out that on-site fans operate in overload mode that can quickly damage their mechanical components. This method provides mining engineers with an easy-to-apply tool for proper adjustment of the operation mode. This improves ventilation efficiency, increases environmental safety, and reduces the underground coal mine operational costs.

Spatial distribution patterns and predictors of fish beta‐diversity in a large dam‐free tributary from a Neotropical floodplain

AbstractUnderstanding how and why fish species composition varies between locations through beta‐diversity has received increased interest recently. The physical structure, extension and environmental characteristics along the river basins influence the dispersal of organisms, which ultimately affects metacommunity dynamics and biodiversity patterns. Here, we aimed to identify major breaks delimiting fish assemblages in a large dam‐free river basin on its main channel, which is almost pristine in hidrology, and to evaluate the influence of geo‐climatic variables in the observed beta‐diversity patterns. We used the statistical techniques Species Composition Interpolation (SCI) and Generalized Dissimilarity Modeling (GDM) to analyse beta‐diversity patterns based on georeferenced fish records from the Ivaí River basin. Ichthyofauna units were delimited, estimating the association between geo‐climatic predictors and beta‐diversity. We identified three major breaks delimiting fish assemblages in the Ivaí River basin (i.e. the upper, middle and lower sections). The fish communities were very distinct at the upper and lower sections, while the middle section was recovered as less differentiated. Turnover was the main component contributing to total beta‐diversity, and dissimilarity in fish species composition was moderately associated with geo‐climatic variables. Fish communities from the Ivaí River basin must be associated with niche‐based processes, and we hypothesize that dispersal processes can be equally significant at the basin scale. The investigations may produce insights on strategies for conservation and restoration of degraded rivers, contributing to the long‐term maintenance of fish communities and to the delimitation of new strategic areas for the effective conservation of freshwater biodiversity.

Improved Forecast Skill Through the Assimilation of Dropsonde Observations From the Atmospheric River Reconnaissance Program

AbstractLandfalling atmospheric rivers (ARs) over the western US are responsible for ∼30%–50% of the annual precipitation, and their accurate forecasts are essential for aiding water management decisions and reducing flood risks. Sparse coverage of conventional observations over the Pacific Ocean, which can cause inadequate upstream initial conditions for numerical weather prediction models, may limit the improvement of forecast skill for these events. A targeted field program called AR Reconnaissance (Recon) was initiated in 2016 to better understand and reduce forecast errors of landfalling ARs at 1–5 days lead times. During the winter seasons of 2016, 2018, and 2019, 15 Intensive Observation Periods (IOPs) sampled the upstream conditions for landfalling ARs. This study evaluates the impact on forecast accuracy of assimilating these dropsonde data. Data denial experiments with (WithDROP) and without (NoDROP) dropsonde data were conducted using the Weather Research and Forecasting model with the Gridpoint Statistical Interpolation four‐dimensional ensemble variational system. Comparisons between the 15 paired NoDROP and WithDROP experiments demonstrate that AR Recon dropsondes reduced the root‐mean‐square error in integrated vapor transport (IVT) and inland precipitation for more than 70% of the IOPs, averaged over all forecast lead times from 1 to 6 days. Dropsondes have improved the spatial pattern of forecasts of IVT and precipitation in all 15 IOPs. Significant improvements in skill are found beyond the short range (1–2 days). IOP sequences (i.e., back‐to‐back IOPs every other day) show the most improvement of inland precipitation forecast skill.