Typhoon Cases Research Articles

Variational All‐Sky Assimilation Framework for MWHS‐II With Hydrometeors Control Variables and Its Impacts on Analysis and Forecast of Typhoon Cases

AbstractAll‐sky radiance assimilation has been extensively developed to provide additional information for numerical weather prediction under cloudy conditions. Microwave radiances are particularly sensitive to hydrometeors, which can be used to initialize hydrometeor directly if the hydrometeor control variables (HCVs) are available. However, the effects of HCVs statistical structure and their multivariate correlation on all‐sky radiance assimilation remain unclear. In this study, five HCVs are introduced into the variational assimilation system. The characteristics of hydrometeor background errors are analyzed, and the combined effect with the observation operator is discussed. Then a 3D Variational all‐sky assimilation framework with HCVs is modified to assimilate Fengyun‐3C/D Microwave Humidity Sounder‐II radiance. It is shown that hydrometeors are initialized by radiance directly, and the thermodynamic fields are adjusted accordingly. The characteristics of multi‐variables increments are associated with both the characteristics of HCVs in background error and the Jacobians in observation operator. Furthermore, cycle assimilation and forecast experiments for three typhoon cases are conducted. It is found that the difference between observed and analyzed brightness temperatures decreases when HCVs are activated, and the hydrometeors analysis fields are more consistent with observations. Additionally, the typhoon intensity forecasts are improved with enhanced double warm‐core and the secondary circulation. This paper analyzes the characteristics of variational all‐sky assimilation framework with HCVs, and demonstrates the potential value of HCVs for variational all‐sky radiance assimilation.

A Localized Quantitative Precipitation Estimation for S-band Polarimetric Radar in Taiwan

Abstract A polarimetric radar quantitative precipitation estimation to estimate rain rate (R) from specific attenuation (A) has been applied in Taiwan's operational Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system since 2016. A 3-yrs' (2016-2018) drop size distribution dataset from an operational Parsivel network was used to derive a localized coefficient as well as the α(K) function in the R(A) scheme for S-band radar, where α is a key parameter in the estimation of A and K is the linear fitted slope of differential reflectivity (ZDR) versus reflectivity (Z). The local DSD data was also used to derive localized R(Z) and R(KDP) relationships, and the relationships were evaluated using radar observations in heavy rain cases. A synthetic QPE combining the localized R(A), R(Z), and R(KDP) relationships is compared to its operational counterpart and showed about 8 % reduction in normalized mean error for the Mei-Yu cases. Typhoon cases exhibited similar improvements by the localized QPE relationships, but showed higher uncertainties than in the Mei-Yu cases. The higher uncertainties in the typhoon QPE verification was likely due to the stronger winds in typhoons than in the Mei-Yu events that caused greater mismatches between the radar observations at an altitude and the gauges at the ground. Overall, the results demonstrated advantages of localized radar rainfall relationships derived from the disdrometer data to improve the accuracy of the operational rainfall estimation products.

Kilometer-scale multi-physics simulations of heavy precipitation events in Northeast China

Despite the fatal impact of heavy precipitation on people’s lives and the social economy, its accurate estimating remains challenging. In this study, we show how to address this issue by kilometer-scale simulations and how to reduce computational costs in Northeast China with the complex terrain and distribution of land and sea. Three typical heavy precipitation events are simulated at 3 km horizontal resolution, and each event is simulated with 24 combinations of schemes (with or without a scale-aware cumulus scheme, three microphysics schemes, and four planetary boundary layer schemes), which are evaluated against gauge observations. Compared to gauge observations, the ensemble mean of simulations of hourly maximum precipitation and average accumulated precipitation outperforms three widely accepted satellite products in the cold vortex and the snowstorm case, and is of comparable accuracy in the typhoon case. Overall, the microphysics scheme significantly impacts the maximum hourly precipitation, whereas the planetary boundary layer scheme has a strong control over the accumulated precipitation. The similarity among different simulations is linked to the level of convective instability's impact on heavy precipitation in each case, which also indicates that conducting 24 simulations can be not necessary. This study uses an ensemble performance estimation technique assuming the impact of different schemes is additive and finds that performing 13 rather than 24 simulations allows finding the best-performing combination of parameterization schemes, which allows for saving almost 50% of computational costs.

Enhanced shear and veer in the Taiwan Strait during typhoon passage

During typhoon passage extreme wind conditions pose a challenge to the structural integrity of wind turbines. Particularly, wind shear and wind veer can influence wind turbine loads. This study investigates how Taiwan’s central mountain range affects wind shear and wind veer in the Taiwan Strait during three westward-moving typhoon cases. The typhoons are simulated with the Weather Research and Forecasting (WRF) model. We find that wind speed, shear, and veer vary over different regions in the Taiwan Strait. In large areas, the simulated wind shear is larger than modeled over the open ocean. In particular, mountain blockage leads to a spatially confined area of several 1000 km2 downstream of Taiwan, that exhibits over several hours strongly modified shear and veer in all three analyzed cases. Shear exponents up to 0.75 and veer between 0.2 and 0.6° m−1 suggest that turbine loads are impacted by the vertical change in the wind field in this area. The shear exponent and wind veer vary strongly with height in the downstream area of Taiwan’s central mountain range. The location of the area with large wind shear and wind veer differs between the three simulated typhoon cases and primarily depends on the latitude of the typhoon track relative to the central mountain range.

Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities

Abstract. There is evidence of an increased frequency of rapid intensification events of tropical cyclones (TCs) in global offshore regions. This will not only result in increased peak wind speeds but may lead to more intense heavy precipitation events, leading to flooding in coastal regions. Therefore, high impacts are expected for urban agglomerations in coastal regions such as the densely populated Pearl River Delta (PRD) in China. Regional climate models (RCMs) such as the Weather Research and Forecasting (WRF) model are state-of-the-art tools commonly applied to predict TCs. However, typhoon simulations are connected with high uncertainties due to the high number of parameterization schemes of relevant physical processes (including possible interactions between the parameterization schemes) such as cumulus (CU) and microphysics (MP), as well as other crucial model settings such as domain setup, initial times, and spectral nudging. Since previous studies mostly focus on either individual typhoon cases or individual parameterization schemes, in this study a more comprehensive analysis is provided by considering four different typhoons of different intensity categories with landfall near the PRD, i.e. Typhoon Neoguri (2008), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Usagi (2013), as well as two different schemes for CU and MP, respectively. Moreover, the impact of the model initialization and the driving data is studied by using three different initial times and two spectral nudging settings. Compared with the best-track reference data, the results show that the four typhoons show some consistency. For track bias, nudging only horizontal wind has a positive effect on reducing the track distance bias; for intensity, compared with a model explicitly resolving cumulus convection, i.e. without cumulus parameterization (CuOFF; nudging potential temperature and horizontal wind; late initial time), using the Kain–Fritsch scheme (KF; nudging only horizontal wind; early initial time) configuration shows relatively lower minimum sea level pressures and higher maximum wind speeds, which means stronger typhoon intensity. Intensity shows less sensitivity to two MP schemes compared with the CuOFF, nudging, and initial time settings. Furthermore, we found that compared with the CuOFF, using the KF scheme shows a relatively larger latent heat flux and higher equivalent potential temperature, providing more energy to typhoon development and inducing stronger TCs. This study could be used as a reference to configure WRF with the model's different combinations of schemes for historical and future TC simulations and also contributes to a better understanding of the performance of principal TC structures.

Data-driven typhoon case retrieval method based on spatiotemporal trajectory similarity and risk preferences of decision-makers

The results of typhoon case retrieval are important for decision-making during emergency situations. Therefore, this study proposes a new typhoon case retrieval method to improve the accuracy of the retrieval results based on the spatiotemporal trajectory similarity and the risk preferences of decision-makers. First, a spatiotemporal trajectory similarity measurement is proposed to generate the path similarity case set. Next, attribute similarity and case similarity measurements are applied to obtain the set of similar cases. Subsequently, a group decision-making model based on fuzzy evidence reasoning is constructed to obtain the risk utility value according to the assessment information and risk preferences of the decision-makers. The risk utility and case similarity are aggregated to obtain the comprehensive utility value and the most appropriate historical typhoon case, thereby generating the emergency alternative. Finally, the effectiveness and feasibility of the proposed method are demonstrated using a typhoon disaster example. The advantages of the proposed method are illustrated through comparative analyses with other methods.

Deep Learning‐Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast

AbstractAccurate offshore surface wind forecasting is crucial for navigation safety and disaster prevention. However, significant biases exist in forecasting sea surface winds due to the uncertainties in estimating sea surface roughness. In this study, we propose a deep learning‐based scheme (DL2023) for estimating sea surface roughness and integrate it into a regionally coupled ocean‐atmosphere‐wave model. Single‐point experiments demonstrate that DL2023 achieves a remarkable 50% reduction in the Root Mean Square Error (RMSE) compared to the four traditional schemes. During five typhoon cases in August 2020, compared to the four traditional schemes, the RMSEs of forecasted surface winds using DL2023 are reduced by 6.02%–14.75%, 11.17%–18.30%, and 11.91%–19.46% at lead times of 24, 48, and 72 hr, respectively. Thus, the DL2023 scheme, trained using data from the Atlantic Ocean, successfully improves the forecast of surface winds over the Northwest Pacific Ocean.

Comparison of Cloud/Rain Band Structures of Typhoon Muifa (2022) Revealed in FY‐3E MWHS‐2 Observations With All‐Sky Simulations

AbstractAll‐sky simulations of brightness temperature (TB) at Microwave Humidity Sounder‐2 (MWHS‐2) channel 15 from Fengyun‐3E (FY‐3E) are generated based on the European Center for Medium‐Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis for four typhoon cases. For Typhoon Muifa (2022), the MWHS‐2 TB observations have a strongest symmetric component within about 210‐km radial distances. Although much weaker, the symmetric component in the GFS all‐sky simulations compares more favorably with that of observations than the ERA5 all‐sky simulations. Due to ice scattering effects at 183.31 ± 7 GHz, spatial distribution patterns of TB observations and all‐sky simulations are quite similar to those of ice water path. The more cloud ice, the lower the TB. Deep convective systems within Muifa are captured by TB observations but not by simulations. Using an objective azimuthal‐spectral‐analysis based center‐fixing algorithm, the center positions of Typhoon Muifa can be well determined from both MWHS‐2 observations and GFS all‐sky simulations, but not from the ERA5 all‐sky TB simulations. Compared with the best track, the average center position errors for Muifa are 16.2, 24.9 and 74.4 km based on FY‐3E MWHS‐2 TB observations, the GFS all‐sky TB simulations, and the ERA5 all‐sky simulations, respectively. The conclusions of TC cloud/rain band structures and center positions of all‐sky simulations using the NCEP‐GFS analysis being better matching with those of FY‐3E MWHS‐2 observations than the ERA5 all‐sky simulations for Typhoon Muifa also hold true for three other typhoons.

Assessment of Typhoon Precipitation Forecasts Based on Topographic Factors

For this paper, a new global atmospheric model (Global-to-Regional Integrated forecast SysTem; GRIST) with improved sub-grid scale orographic parameterization was verified and assessed, with an emphasis on the precipitation caused by typhoons. Four typical typhoon cases were selected for the verification of the model. The results indicate that, compared to the control experiments, the sensitivity experiments consistently simulated the trends in the three-hour cumulative precipitation changes and the high-value regions of total precipitation better. However, the improved experiments only had an ameliorating effect on the cumulative precipitation modelling biases for Typhoon LEKIMA and Typhoon HAGUPIT, not all of them. Precipitation bias is smaller on flat land than that on mountainous land, but the precipitation bias on windward/leeward slopes depends on the typhoon case. Precipitation modelling accuracy varies considerably between flat and mountainous terrain but very little between windward and leeward slopes. The precipitation simulation is poor for all terrains, with large precipitation thresholds in three typhoon cases, but for Typhoon HOTA, after improving the terrain, the model has the ability to forecast the heavy rainfall scenarios of the mountainous terrain.

Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models

In this study, the impact of assimilating MWHS2 radiance data under different background conditions on the analyses and deterministic prediction of the Super Typhoon Muifa case, which hit China in 2022, was explored. The fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and the Global Forecast System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP) were used as the background fields. To assimilate the Microwave Humidity Sounder II (MWHS2) radiance data into the numerical simulation experiments, the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system were employed. The results show that after the data assimilation, the standard deviation and root-mean-square error of the analysis significantly decrease relative to the observation, indicating the effectiveness of the assimilation process with both background fields. In the MWHS_GFS experiment, a subtropical high-pressure deviation to the east is observed around the typhoon, resulting in its northeast movement. In the differential field of the MWHS_ERA experiment, negative sea-level pressure differences around the typhoon are observed, which increases its intensity. In the deterministic predictions, assimilating the FY3D MWHS2 radiance data reduces the typhoon track error in the MWHS_GFS experiment and the typhoon intensity error in the MWHS_ERA experiment. In addition, it is found that the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for Tovs (RTTOV) model show similar performance in assimilating MWHS2 radiance data for this typhoon case. It seems that the data assimilation experiment with the CRTM significantly reduces the typhoon track error than the experiment with the RTTOV model does, while the intensity error of both experiments is rather comparable.

Assessing code-based design wind loads for offshore wind turbines in China against typhoons

In the areas prone to severe typhoons, such as the south and southeast China waters, offshore wind turbine design will most likely fall into Class-S, but the current design standards have not treated much detail on the technical parameters under the typhoon-induced conditions to guide the structural design. This study set out to investigate the feasibility of extreme wind conditions in existing standards for offshore wind turbines in the south and southeast China waters. The extreme wind conditions in existing standards and actual typhoon cases in the south and southeast China waters were used to compare and analyze the typhoon-induced wind effect on offshore wind turbines. This study supports evidence from previous views that the extreme wind condition specified by existing design standards does not provide a sufficient safety margin for the south and southeast China waters use. Another interesting finding is that from the perspective of the typhoon-resistant design of large-scale offshore wind turbines in the future, it is more inclined to abandon the height of the hub and use the height of 10m as the reference wind speed height to obtain a more reasonable wind speed field as input conditions. The insights gained from this study may be of assistance to the typhoon-resistant design of customized offshore wind turbines in the south and southeast China waters and would help reduce the risks and economic losses of Class-S wind turbines in other typhoon-prone areas.

Comparing water vapor transport during precipitation processes in the pre-flood period of South China and precipitation processes of typhoons in the post-flood period of South China: A case study

The typical precipitation processes in the pre-flood of South China and precipitation processes of typhoons in the post-flood period of South China have different water vapor transport characteristics. In this research, the water vapor transport characteristics of the representative precipitation cases in the two circumstances were compared using the fifth generation ECMWF reanalysis (ERA5) and Late Run of Integrated Multi-satellitE Retrievals for GPM (IMERG-L). The water vapor transport channel of the precipitation process of the typhoon was stronger and higher than that in the pre-flood period of South China, and the distribution of water vapor flux convergence was also higher. The maximum value of water vapor flux of pre-flood case was related to the wind speed and specific humidity both in the boundary layer (950 hPa). For the typhoon case, the maximum value of water vapor flux was related to the wind speed in the boundary layer, whereas it was related to the specific humidity in the middle and low layer (700 hPa). These distinctions can help us quickly find the probably locations and impact factors of the maximum values of water vapor flux during precipitation processes in the pre-flood of South China and precipitation processes of typhoons in the post-flood period of South China on different pressure surfaces to the greatest extent and predict the intensities of different water vapor transport channels.

Typhoon Case Comparison Analysis Between Heterogeneous Many-Core and Homogenous Multicore Supercomputing Platforms

Typhoon Case Comparison Analysis Between Heterogeneous Many-Core and Homogenous Multicore Supercomputing Platforms

The impacts of assimilating CFOSAT scatterometer winds for Typhoon cases based on real-time rain quality control

Rain is the main factor influencing the Ku-band scatterometer wind quality. The operational quality control (QC) scheme of the scatterometer wind retrieval procedure cannot effectively distinguish rain from high winds, limiting the optimal assimilation of Ku-band scatterometer winds in numerical weather prediction. To compensate for the inadequate operational QC of CFOSAT scatterometer (CSCAT) data, a real-time rain quality control (RRQC) method in data assimilation was proposed. Based on real-time rain forecasts, the RRQC method aims to screen and eliminate rain-affected CSCAT data. To begin, the impact of rain on CSCAT data for the entire year 2019 was analyzed. The CSCAT wind speed standard deviation (STD) and bias are found to increase with rain rate, and when the rain rate exceeds 2.5 mm/h, the STD is greater than the CSCAT-designed accuracy (2 m/s). Following that, two Typhoon cases, namely Lekima (2019) and In-fa (2021), were studied to assess the RRQC method's impact on Typhoon forecasting. It shows that assimilating CSCAT data with the RRQC method improves wind analysis and forecast, and reduces the mean track error. Finally, Typhoon Lekima was further diagnosed. It is found that the CSCAT data eliminated by RRQC are mainly distributed around the Lekima rainband with large wind speed observation minus background (OMB) absolute values. The results indicate that RRQC can identify rain contaminations in the CSCAT data and thus improve the analysis and forecast.

A Bagged-Tree Machine Learning Model for High and Low Wind Speed Ocean Wind Retrieval From CYGNSS Measurements

This paper presents two empirical models, the low wind bagged trees (LWBT) and high wind bagged trees (HWBT) ensemble models to estimate ocean surface wind speed using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data. The models are empirically trained using NASA’s Cyclone GNSS (CYGNSS) mission level 1 data (version 2.1). The truth label for the LWBT model is the wind speed product derived from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 and Global Data Assimilation System (GDAS), while the label for the HWBT model is wind speed measurements from Stepped Frequency Microwave Radiometer (SFMR). Testing results show that the LWBT and HWBT models achieved global wind speed retrieval root-mean-square-error (RMSE) of ~1.5 m/s and ~1.4 m/s, respectively, corresponding to an improvement of 29% and 65% with respect to the CYGNSS Level 2 standard wind speed product. The maximum bias is reduced by 65% and 60% for LWBT and HWBT over the Level 2 wind speeds, respectively. Two typhoon case studies are presented to corroborate the model performances and their retrieved wind speeds are consistent with reports from World Meteorological Organization (WMO) and with the measurement provided by the Huangmao Zhou (HMZ) weather station.

A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case

Abstract. Integrating the hybrid and multiscale analyses and the parallel computation is necessary for current data assimilation schemes. A local data assimilation method, Local DA, is designed to fulfill these needs. This algorithm follows the grid-independent framework of the local ensemble transform Kalman filter (LETKF) and is more flexible in hybrid analysis than the LETKF. Local DA employs an explicitly computed background error correlation matrix of model variables mapped to observed grid points/columns. This matrix allows Local DA to calculate static covariance with a preset correlation function. It also allows the conjugate gradient (CG) method to be used to solve the cost function and allows localization to be performed in model space, observation space, or both spaces (double-space localization). The Local DA performance is evaluated with a simulated multiscale observation network that includes sounding, wind profiler, precipitable water vapor, and radar observations. In the presence of a small-size time-lagged ensemble, Local DA can produce a small analysis error by combining multiscale hybrid covariance and double-space localization. The multiscale covariance is computed using error samples decomposed into several scales and independently assigning the localization radius for each scale. Multiscale covariance is conducive to error reduction, especially at a small scale. The results further indicate that applying the CG method for each local analysis does not result in a discontinuity issue. The wall clock time of Local DA implemented in parallel is halved as the number of cores doubles, indicating a reasonable parallel computational efficiency of Local DA.

Numerical Investigation Of The Uncertainty Of Typhoon Wave Parameters Retrieval Using HF Radar

The aim of this study was to identify the sources of uncertainties of typhoon wave field monitoring using HF radar and to quantitatively assess the bias of the wave parameters, such as significant wave height and mean period, retrieved under various conditions. The strategy was to apply a purely numerical simulation of the Doppler-range spectra and then compare the estimation results to the target. For the quantitative investigation, a numerical test-bed was established. The test-bed was used to simulate the Doppler spectra based on Barrick’s (1972) theory by using the input of the directional wave spectra. The typhoon wave spectra were hindcasted using a third-generation wave model. Initially, the uncertainty source from Barrick’s theory was identified when comparing the deviations of the estimation results obtained from the idealized case of steady and homogenous fields. The accuracy of the wave height and the mean period was found to be significantly influenced by the angle between the radar-looking direction and the wave direction. Furthermore, two conceptual numerical experiments were designed to evaluate the uncertainties owing to the rotation and the translation of the typhoon wind fields, respectively. The first design focussed on the upper-right quadrant around the maximum wind radius of the typhoon using a virtual radar network that moved along with the typhoon. The second one was a more realistic design in that the virtual radar stations were located on the coastline. The scatter index of the wave height estimated from the second design was found to be approximately twice larger than that obtained using the first design. It was 25% larger for the uncertainty of the mean period. This demonstrated that except for the error from theory, the uncertainty of the estimated wave parameters in type 1 was influenced by the change in the wave generated by the wind field, while that of type 2 was affected by the complicated typhoon wavefield, including the mixed wind waves and swells. The results showed that the error of 0.02 of the scatter index in the case of the wave height could be identified even when no system noise was considered. This error was attributed to the simplification of the coupling coefficient and the weighting function in Barrick’s theory. The error was direction dependent and non-negligible. For the typhoon cases, the heterogeneity and rapid changes in the spatial distribution of the wavefield under the influence of the rotating wind fields were the challenging

Radiative Effects on Tropical Cyclone Development in Different Life Stages

Abstract A tropical cyclone (TC) is a powerful, rotating storm that typically originates over warm tropical oceans and creates strong winds and heavy rain; it is usually a natural disaster with respect to human life and property if it moves over land. This work examines effects of varying radiative forcing on the evolution of two typhoon cases—Typhoon Lionrock (2016) and Typhoon Hagibis (2019)—with the Weather Research and Forecasting (WRF) Model. Hagibis was a rapidly intensifying and quickly moving TC, whereas Lionrock gradually developed and was slow moving. Numerous sensitivity experiments in which shortwave and longwave radiative heating rates were modified were conducted. This study examined latent heating and radiative heating for each experiment. Substantial differences between the sensitivity simulation members indicated that radiative effects can strongly influence TC development. The analysis of diabatic heating sources shows that, before eyewall formation, the differential cooling effect, which indicates that longwave cooling rates between cloud clusters and clear sky differ, can promote low-level inflow and increase relative humidity in the cloud clusters. If the initial relative humidity is low, this effect becomes important because, without differential cooling, the relative humidity remains low, which can promote the generation of cold pools that will prevent cyclone development. After eyewall formation, both the change in temperature lapse rate due to a vertical gradient of radiative heating/cooling and the change in the warm core due to radiative heating/cooling can affect the intensity of a TC; however, the net effect may depend on the magnitude of these influences.

Spatial Gap-Filling of GK2A Daily Sea Surface Temperature (SST) around the Korean Peninsula Using Meteorological Data and Regression Residual Kriging (RRK)

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.

Drivers of Phytoplankton Variability in and Near the Pearl River Estuary, South China Sea During Typhoon Hato (2017): A Numerical Study

AbstractThe Pearl River Estuary and adjacent seas in South China Sea are frequently affected by tropical cyclones (TCs). Previous in situ and remote sensing studies have found that typhoons can enhance phytoplankton biomass and induce blooms in this region. However, the mechanistic links between phytoplankton blooms and typhoons have not been well understood due to the interplay of multiple complex processes along the land‐ocean‐atmosphere interface. Here, we constructed an integrated modeling system with the marine ecosystem and sediment components for the China Great Bay Area. By using the integrated modeling system, we quantitatively investigated the phytoplankton response to hydrological conditions variations under Typhoon Hato (2017), a strong typhoon case. Passive tracer experiments showed that with high river discharge induced by heavy rainfall, the residence time within Lingding Bay is as short as 15 days, less than half of that under the climatological discharge. The increase in freshwater pulse washes out the phytoplankton biomass within Lingding Bay. While for the offshore region, the analysis of various source and sink terms for the offshore region's post‐typhoon response showed that the increase of phytoplankton biomass in the first week was because of the uplift of nutrient‐rich subsurface water, while in the second week, it was due to the seaward propagation of nearshore high phytoplankton biomass water. While riverine nutrients support phytoplankton growth in the third week, a large part of phytoplankton biomass was lost to zooplankton grazing, showing the system shifted from the bottom‐up control to the top‐down control.