Search Results (281)

Part I of this two-part documentation of the ECMWF ensemble (ECEPS) new tropical cyclone track and intensity forecasting challenges during the 2024 western North Pacific season described four typhoons that started well to the south of an unfavorable external environment north of 20° N. In this Part II, five other 2024 season typhoons that formed and intensified near and north of 20° N are documented. One change is that the Cooperative Institute for Meteorological Satellite Studies ADT + AIDT intensities derived from the Himawari-9 satellite were utilized for initialization and validation of the ECEPS intensity forecasts. Our first objective of providing earlier track and intensity forecast guidance than the Joint Typhoon Warning Center (JTWC) five-day forecasts was achieved for all five typhoons, although the track forecast spread was large for the early forecasts. For Marie (06 W) and Ampil (08 W) that formed near 25° N, 140° E in the middle of the unfavorable external environment, the ECEPS intensity forecasts accurately predicted the ADT + AIDT intensities with the exception that the rapid intensification of Ampil over the Kuroshio ocean current was underpredicted. Shanshan (11 W) was a challenging forecast as it intensified to a typhoon while being quasi-stationary near 17° N, 142° E before turning to the north to cross 20° N into the unfavorable external environment. While the ECEPS provided accurate guidance as to the timing and the longitude of the 20° N crossing, the later recurvature near Japan timing was a day early and 4 degrees longitude to the east. The ECEPS provided early, accurate track forecasts of Jebi’s (19 W) threat to mainland Japan. However, the ECEPS was predicting extratropical transition with V_max ~35 kt when the JTWC was interpreting Jebi’s remnants as a tropical cyclone. The ECEPS predicted well the unusual southward track of Krathon (20 W) out of the unfavorable environment to intensify while quasi-stationary near 18.5° N, 125.6° E. However, the rapid intensification as Krathon moved westward along 20° N was underpredicted. Full article

Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. This study presents a novel algorithm to retrieve day and night CBH using infrared observations of the geostationary Advanced Himawari Imager (AHI). The algorithm is featured by integrating deep learning techniques with a physical model. The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. Then, a physical model is introduced to relate cloud geometric thickness (CGT) to CWP by constructing a look-up table of effective cloud water content (ECWC). Thus, the CBH can be obtained by subtracting CGT from CTH. The results demonstrate good agreement between our AHI CBH retrievals and the spaceborne active remote sensing measurements, with a mean bias of −0.14 ± 1.26 km for CloudSat-CALIPSO observations at daytime and −0.35 ± 1.84 km for EarthCARE measurements at nighttime. Additional validation against ground-based millimeter wave cloud radar (MMCR) measurements further confirms the effectiveness and reliability of the proposed algorithm across varying atmospheric conditions and temporal scales. Full article

Agriculture accounts for 29% of the Gross Domestic Product of the Nusa Tenggara Islands (NTIs). However, recurring agricultural droughts pose a major threat to the sustainability of agriculture in this region. The interplay between precipitation, solar radiation, and surface temperature as meteorological factors plays a key role in affecting vegetation (Soil-Adjusted Vegetation Index) and agricultural drought (Temperature Vegetation Dryness Index) in the NTIs. Based on the analyses of interplay with temporal lag, this study investigates the effect of each factor on agricultural drought and attempts to provide early warnings regarding drought in the NTIs. We collected surface information data from Moderate-Resolution Imaging Spectroradiometer (MODIS). Meanwhile, rainfall was estimated from Himawari-8 based on the INSAT Multi-Spectral Rainfall Algorithm (IMSRA). The results showed reliable performance for 8-day and monthly scales against gauges. The drought analysis results reveal that the NTIs suffer from mild-to-moderate droughts, where cropland is the most vulnerable, causing shifts in the rice cropping season. The driving factors could also explain >60% of the vegetation and surface-dryness conditions. Furthermore, our monthly and 8-day TVDI estimation models could capture spatial drought patterns consistent with MODIS, with coefficient of determination (R²) values of more than 0.64. The low error rates and the ability to capture the spatial distribution of droughts, especially in open-land vegetation, highlight the potential of these models to provide an estimation of agricultural drought. Full article

Autonomous unmanned surface vehicles (USVs) offer transformative potential for collecting marine meteorological data under extreme weather conditions, yet their capability to provide reliable solar radiation measurements during typhoons remains underexplored. This study evaluates shortwave downward radiation (SWDR) data obtained by a solar-powered USV (developed by IAP/CAS, Beijing, China) that successfully traversed Typhoon Sinlaku (2020), compared with Himawari-8 satellite products. The SUSV acquired 1 min resolution SWDR measurements near the typhoon center, while satellite data were collocated spatially and temporally for validation. Results demonstrate that the USV maintained uninterrupted operation and power supply despite extreme sea states, enabling continuous radiation monitoring. After averaging, high-frequency SWDR data exhibited minimal bias relative to Himawari-8 to mitigate wave-induced attitude effects, with a mean bias error (MBE) of 13.64 W m⁻² under cloudy typhoon conditions. The consistency between platforms confirms the SUSV’s capacity to deliver accurate in situ radiation data where traditional observations are scarce. This work establishes that autonomous SUSVs can critically supplement satellite validation and improve radiative transfer models in typhoon-affected oceans, addressing a key gap in severe weather oceanography. Full article

Accurate estimation of ambient PM_2.5 concentrations is crucial for assessing air quality and health risks, particularly in regions with limited ground-based monitoring. Satellite-retrieved data products, such as top-of-atmosphere reflectance (TOAR) and aerosol optical depth (AOD), are widely used for PM_2.5 estimation. However, complex atmospheric conditions cause retrieval gaps in TOAR and AOD products, limiting their reliability. This study introduced a spatiotemporal convolutional approach to fill sampling gaps in TOAR and AOD data from the Himawari-8 geostationary satellite over the Yangtze River Delta (YRD) in 2016. Four machine-learning models (random forest, extreme gradient boosting, gradient boosting, and support vector regression) were used to estimate hourly PM_2.5 concentrations by integrating gap-filled and original TOAR and AOD data with meteorological variables. The random forest model trained on gap-filled TOAR data yielded the highest predictive accuracy (R² = 0.75, RMSE = 18.30 μg m⁻³). Significant seasonal variations in PM_2.5 estimates were found, with TOAR-based models outperforming AOD-based models. Furthermore, we observed that a substantial portion of the YRD population in non-attainment areas is at risk of cardiovascular disease due to chronic PM_2.5 exposure. This study suggests that TOAR-based models offer more reliable PM_2.5 estimates, enhancing air-quality assessments and public health-risk evaluations. Full article

A new quantitative precipitation estimation (QPE) method for Himawari-9 (H9) and Fengyun-4B (FY4B) satellites has been developed based on cloud top brightness temperature (TBB). The 24-hour, 6-hour, and hourly rainfall estimates of H9 and FY4B have been compared with rain gauge datasets and precipitation estimation data from the GPM IMERG V07 (IMERG) and Global Precipitation Satellite (GSMaP) products, especially based on the case study of landfalling super typhoon “Doksuri” in 2023. The results indicate that the bias-corrected QPE algorithm substantially improves precipitation estimation accuracy across multiple temporal scales and intensity categories. For extreme precipitation events (≥100 mm/day), the FY4B-based estimates exhibit markedly better performance. Furthermore, in light-to-moderate rainfall (0.1–24.9 mm/day) and heavy rain to rainstorm ranges (25.0–99.9 mm/day), its retrievals are largely comparable to those from IMERG and GSMaP, demonstrating robust consistency across varying precipitation intensities. Therefore, the new QPE retrieval algorithm in this study could largely improve the accuracy and reliability of satellite precipitation estimation for extreme weather events such as typhoons. Full article

Satellite-based precipitation products (SPPs) have become a crucial source of quantitative global precipitation data. Geostationary Orbit (GEO) satellites provide high spatiotemporal resolution but tend to have lower accuracy, while Low Earth Orbit (LEO) satellites provide more precise precipitation estimates but suffer from lower temporal resolution due to their limited observation frequency. This study proposes an efficient algorithm for integrating and enhancing precipitation estimates from multiple satellite observations. The target domain includes the Full Disk (FD) and the extended East Asia (EA) regions, both of which are observable by GEO satellites, such as Himawari-8, serving as the GEO platform in this study. The algorithm involves four steps: pre-data preparation, LEO morphing, adjustment, and final merging. It produces Early and Late composite products with 10-min temporal and up to 2 km spatial resolution and significantly reduces latency compared to IMERG. Specifically, the Early and Late products can be generated with approximate latencies of 90 min and 270 min, respectively—much faster than Integrated Multi-satellite Retrievals for GPM (IMERG)’s Early (4-h) and Late (14-h) products. A key feature of the proposed method is the use of accuracy-based weighting derived from radar-based validation, enabling dynamic merging that reflects the reliability of each satellite observation. Statistical validation using Global Telecommunication System (GTS) precipitation data confirmed the positive impact of the proposed bias correction and merging method. In particular, the Late product achieved accuracy comparable to or higher than that of IMERG Early and IMERG Late, despite its significantly shorter latency. However, its accuracy was still lower than that of IMERG Final, which benefits from additional gauge-based correction but is released with a delay of several months. Full article

This study presents an objective scheme to construct hurricane bogus vortices based on satellite microwave scatterometer observations of sea surface wind vectors. When specifying a bogus vortex using Fujita’s formula, the required parameters include the center position and the radius of the maximum gradient of sea level pressure (R₀). We first propose determining the tropical cyclone (TC) center position as the cyclonic circulation center obtained from sea surface wind observations and then establishing a regression model between R₀ and the radius of 34-kt sea surface wind of scatterometer observations. The radius of 34-kt sea surface wind (R₃₄) is commonly used as a measure of TC size. The center positions determined from HaiYang-2B/2C/2D Scatterometers, MetOp-B/C Advanced Scatterometers, and FengYun-3E Wind Radar compared favorably with the axisymmetric centers of hurricane rain/cloud bands revealed by Advanced Himawari Imager observations of brightness temperature for the western Pacific landfalling typhoons Doksuri, Khanun, and Haikui in 2023. Furthermore, regression equations between R₀ and the scatterometer-determined radius of 34-kt wind are developed for tropical storms and category-1, -2, -3, and higher hurricanes over the Northwest Pacific (2022–2023). The bogus vortices thus constructed are more realistic than those built without satellite sea surface wind observations. Full article

Atmospheric aerosols play an important role in the ecological environment, climate change, and human health. Aerosol optical depth (AOD) is the main measurement of aerosols. The next-generation geostationary satellite Himawari-8, loaded with the Advanced Himawari Imager (AHI), provides observation-based estimates of the hourly AOD. However, a highly accurate evaluation of AOD using AHI is still limited. In this paper, we establish a Stacked Denoising AutoEncoder (SDAE) model to retrieve highly accurate AOD using AHI. We explore the SDAE to retrieve AOD by taking the ground-observed AOD as the output and taking the AHI image, the month, hour, latitude, and longitude as the input data. This approach was tested in the Hubei province of China. Traditional machine learning methods such as Extreme Learning Machines (ELMs), BackPropagation Neural Networks (BPNNs), and Support Vector Machines (SVMs) are also used to evaluate model performance. The results show that the proposed method has the highest accuracy. We also compare the proposed method with ground-observed AOD measurements at the Wuhan University site, showing good consistency between the satellite-retrieved AOD and the ground-observed value. The study of the spatiotemporal change pattern of the hourly AOD in the Hubei province shows that the algorithm has good stability in the face of changes in the angle and intensity of sunlight. Full article

This study uses GPM DPR and Himawari-8 cloud-top infrared data to classify the precipitating cloud (PC) into three life stages: developing, mature, and dissipating. Based on GPM DPR data from April to June 2018–2022, this research investigates the microphysical features of convective and stratiform precipitation over South China. The precipitation generated by the developing stage of the PC contains the largest proportion of convective precipitation, the largest precipitation area in the mature stage of PC, and the smallest precipitation area with the lowest convective precipitation proportion in the dissipating stage of the PC. For stratiform precipitation generated by the developing PC, the height of 0 °C level is marginally above the top height of Bright Band (BB), with both heights aligning in altitude during the mature and dissipating stages of the PC. The mass-weighted mean diameter (D_m) peaks at 1.2 mm below the BB, and near-surface D_m is positively correlated with the storm top height. For convective precipitation, raindrops with D_m of 1.9 mm and those exceeding 3.0 mm predominate. Notably, the near-surface D_m shows a positive correlation with storm top height, with the correlation coefficient for convective precipitation being greater than that for stratiform precipitation. Significantly, the average liquid and non-liquid water paths are larger in the dissipating stage as compared to the developing stage for both precipitation types. These findings suggest enhanced precipitation efficiency in South China and underscore the critical importance of stage-specific analyses in comprehending precipitating cloud microphysics. Full article

High-resolution (2 km), high-frequency (hourly) SST data of the Advanced Himawari Imager (AHI) flown onboard the Japanese Himawari-8 geostationary satellite were used to derive the monthly climatology of temperature fronts in the South China Sea. The SST data from 2015 to 2022 were processed with the Belkin–O’Reilly algorithm to generate maps of SST gradient magnitude GM. The GM maps were log-transformed to enhance contrasts in digital maps and reveal additional features (fronts). The combination of high-resolution, cloud-free, four-day-composite SST imagery from AHI, the advanced front-preserving gradient algorithm BOA, and digital contrast enhancement with the log-transformation of SST gradients allowed us to identify numerous mesoscale/submesoscale fronts (including a few fronts that have never been reported) and document their month-to-month variability and spatial patterns. The spatiotemporal variability of SST fronts was analyzed in detail in five regions: (1) In the Taiwan Strait, six fronts were identified: the China Coastal Front, Taiwan Bank Front, Changyun Ridge Front, East Penghu Channel Front, and Eastern/Western Penghu Islands fronts; (2) the Guangdong Shelf is dominated by the China Coastal Front in winter, with the eastern and western Guangdong fronts separated by the Pearl River outflow in summer; (3) Hainan Island is surrounded by upwelling fronts of various nature (wind-driven coastal and topographic) and tidal mixing fronts; in the western Beibu Gulf, the Red River Outflow Front extends southward as the Vietnam Coastal Front, while the northern Beibu Gulf features a tidal mixing front off the Guangxi coast; (4) Off SE Vietnam, the 11°N coastal upwelling gives rise to a summertime front, while the Mekong Outflow and associated front extend seasonally toward Cape Camau, close to the Gulf of Thailand Entrance Front; (5) In the Luzon Strait, the Kuroshio Front manifests as a chain of three fronts across the Babuyan Islands, while west of Luzon Island a broad offshore frontal zone persists in winter. The summertime eastward jet (SEJ) off SE Vietnam is documented from five-day mean SST data. The SEJ emerges in June–September off the 11°N coastal upwelling center and extends up to 114°E. The zonally oriented SEJ is observed to be located between two large gyres, each about 300 km in diameter. Full article

A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As Typhoon Gaemi (05 W) was moving poleward into an unfavorable environment north of 20° N, a sharp westward turn to cross Taiwan was a challenge to forecast. The pre-Yagi (12 W) westward turn across Luzon Island, re-formation, and then extremely rapid intensification prior to striking Hainan Island were challenges to forecast. The slow intensification of Bebinca (14 W) after moving poleward across 20° N into an unfavorable environment was better forecast by the ECEPS than by the Joint Typhoon Warning Center (JTWC), which consistently over-predicted the intensification. An early westward turn south of 20° N by Kong-Rey (23 W) leading to a long westward path along 17° N and then a poleward turn to strike Taiwan were all track forecasting challenges. Four-dimensional COAMPS-TC Dynamic Initialization analyses utilizing high-density Himawari-9 atmospheric motion vectors are proposed to better define the TC intensities, vortex structure, and unfavorable environment for diagnostic studies and as initial conditions for regional model predictions. In Part 2 study of selected 2024 season TCs that started north of 20° N, more challenging track forecasts and slow intensification rates over an unfavorable TC environment will be documented. Full article

Wet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands. Full article

The accurate forecasting of time, intensity, and spatial distribution is fundamental to weather prediction. However, the limitations of numerical weather prediction (NWP) models, as well as uncertainties in inital conditions, often lead to temporal biases in forecasts. This study addresses these biases by employing visible reflectance data from the Himawari-9/AHI satellite and RTTOV (TOVS radiation transfer) simulations derived from CMA-MESO model outputs. The time-shift method was applied to analyze two precipitation events—20 October 2023 and 30 April 2024—in order to assess its impact on precipitation forecasts. The results indicate the following: (1) the time-shift method improved cloud simulations, necessitating a 30 min advance for Case 1 and a 3.5 h delay for Case 2; (2) time-shifting reduced the standard deviation of observation-minus-background (OMB) bias in certain regions and enhanced spatial uniformity; (3) the threat score (TS) demonstrated an improvement in forecast accuracy, particularly in cases exhibiting significant movement patterns. The comparative analysis demonstrates that the time-shift method effectively corrects temporal biases in NWP models, providing forecasters with a valuable tool to optimize predictions through the integration of high-temporal- and spatial-resolution visible light data, thereby leading to more accurate and reliable weather forecasts. Full article

Wind field retrieval, a crucial component of weather forecasting, has been significantly enhanced by recent advances in deep learning. However, existing approaches that are primarily focused on wind speed retrieval are limited by their inability to achieve real-time, full-coverage retrievals at large scales. To address this problem, we propose a novel multi-scale fusion retrieval (MFR) method, leveraging geostationary observation satellites. At the mesoscale, MFR incorporates a cloud-to-wind transformer model, which employs local self-attention mechanisms to extract detailed wind field features. At large scales, MFR incorporates a multi-encoder coordinate U-net model, which incorporates multiple encoders and utilises coordinate information to fuse meso- to large-scale features, enabling accurate and regionally complete wind field retrievals, while reducing the computational resources required. The MFR method was validated using Level 1 data from the Himawari-8 satellite, covering a geographic range of 0–60°N and 100–160°E, at a resolution of 0.25°. Wind field retrieval was accomplished within seconds using a single graphics processing unit. The mean absolute error of wind speed obtained by the MFR was 0.97 m/s, surpassing the accuracy of the CFOSAT and HY-2B Level 2B wind field products. The mean absolute error for wind direction achieved by the MFR was 23.31°, outperforming CFOSAT Level 2B products and aligning closely with HY-2B Level 2B products. The MFR represents a pioneering approach for generating initial fields for large-scale grid forecasting models. Full article