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- The Black Array of Broadband Absolute Radiometers Earth Radiation Imager:
Science Requirements, Instrument Design, and Concept of Operations-
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Abstract: The Black Array of Broadband Absolute Radiometers Earth Radiation Imager (BABAR-ERI) is a small, adaptable nadir-pointed pushbroom imager to measure Earth-leaving broadband radiance from 0.3 µm to 100 µm with higher information content than is currently measured by reducing radiometric uncertainty and enabling cloud-resolving spatial resolution. The three-instrument BABAR-ERI suite fits a 12U CubeSat form factor and contains co-registered science telescope channels for measuring shortwave (0.3 µm to 4.5 µm band) and total radiance (0.3 µm to 100 µm band), dual-channel on-board radiance stability monitors, and a visible-wavelength camera. Novel, 1 × 32 element, electrical-substitution radiometer pixels image the shortwave and total radiance in 1 km × 1 km co-registered ground footprints; longwave radiance (4.5 µm to 100 µm band) is derived from subtraction of the shortwave and total radiance. The dual-channel onboard stability monitors are radiance standard detectors, and their measurements, acquired concurrently with the science telescopes and at much different duty cycles for the dual channels, will be used to track and correct the degradation of the science channels. The single-channel, mid-visible camera facilitates geolocation pointing knowledge and provides scene context information and sub-pixel variability to facilitate measurement stability studies and enable process-level science studies at high spatial resolution. The detectors for the science channels and stability monitors are absolute, ambient-temperature, micro-fabricated, electrical-substitution radiometers with near-perfect optical absorptance across the measurement range from vertically aligned carbon nanotubes. The BABAR-ERI science channels will be characterized over the full measurement range and for variable Earth scenes and deep space temperatures during extensive ground calibrations.
PubDate: 2025-07-01
- An Extension of Conditional Nonlinear Optimal Perturbation in the Time
Dimension and Its Applications in Targeted Observations-
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Abstract: The Conditional Nonlinear Optimal Perturbation (CNOP) method works essentially for conventional numerical models; however, it is not fully applicable to the commonly used deep-learning forecasting models (DLMs), which typically input multiple time slices without deterministic dependencies. In this study, the CNOP for DLMs (CNOP-DL) is proposed as an extension of the CNOP in the time dimension. This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations. The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM. The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time. Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself, but also for random perturbations. Therefore, this approach holds potential for guiding practical field campaigns. Notably, forecast errors are more sensitive to time than to location in the sensitive area. It highlights the crucial role of identifying the time of the sensitive area in targeted observations, corroborating the usefulness of extending the CNOP in the time dimension.
PubDate: 2025-06-25
- Aircraft Observations of Ice-Phase Microphysical Characteristics in
Stratiform Clouds over the Qilian Mountains in Northwestern China-
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Abstract: The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data. The stratiform cloud system developed under southwesterly flows at 500 hPa and was affected locally by topography. Synoptic features and aircraft observations revealed strengthened cloud development on the leeward slope. The ice particle habits and microphysical processes at heights of 6–8 km were investigated. The cloud system was characterized by extremely low supercooled liquid water content at temperatures between −4°C and −17°C. The ice particle concentrations ranged predominantly from 10 to 30 L−1, corresponding to ice water content ranging from 0.01 to 0.05 g m−3. Active ice aggregation was observed at temperatures colder than −10°C. The windward side of the cloud system exhibited weaker development and two distinct cloud layers. Intense orographic uplift on the leeward slope enhanced ice particle aggregation. The clouds on the leeside presented lower ice particle concentrations but larger sizes than those on the windward side. The influence of aggregation on the ice particle size distribution was reflected in two main aspects. One aspect was the bimodal spectra at −16°C, with the first peak at 125 µm and subpeak at 400–500 µm; the other was the broadened size spectra at −13°C due to significant aggregation of dendrites.
PubDate: 2025-06-25
- Tropical Sea Surface Warming Patterns and Tropical Cyclone Activity: A
Review-
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Abstract: Recent studies identify large uncertainties in the projections of tropical cyclone (TC) activity due to discrepancies in tropical Pacific sea surface temperature (SST) warming patterns. While observational datasets consistently reveal a La Niña-like warming pattern [0.15°C–0.25°C (10 yr)−1 relative cooling in the eastern equatorial Pacific], over 80% of CMIP6 models project an erroneous El Niño-like trend. These discrepancies arise from biases in cloud feedbacks, Walker circulation strength, and oceanic upwelling processes. This review examines the key mechanisms shaping observed versus modeled warming patterns, evaluates the complex link between tropical SST patterns and TC activity, and explores the feasibility of storm-resolving models for improving TC projections. We propose that pattern-conditioned TC projections using convection-permitting models, alongside physics-informed interpretations, offer a path forward in reducing uncertainties in future climate predictions.
PubDate: 2025-06-25
- Oceanic Eddy Kinetic Energy in the Spectral Space Regulated by Mesoscale
Air–Sea Heat Exchange in the Kuroshio Extension-
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Abstract: Mesoscale air–sea interactions play a critical role in damping eddy activities. However, how mesoscale heat flux influences the distribution of eddy kinetic energy (EKE) in the wavenumber space remains unclear. In this study, we investigate the EKE and temperature variance (Tvar) budgets in the Kuroshio Extension (KE) region using wavenumber spectral analysis based on 1/10° coupled climate simulations. These simulations include a standard high-resolution simulation and a smoothed simulation that overlooks mesoscale heat flux. By comparing the differences between these models, we confirm that air-sea heat exchange significantly dissipates Tvar. Neglecting mesoscale heat flux results in a 60% underestimation of the Tvar damping rate, which in turn increases energy transfer to EKE through the vertical buoyancy flux by 22%. This enhanced vertical buoyancy flux leads to a 20% higher EKE level and larger energy budget terms, particularly in the diffusion term, which is closely related to wind power. Furthermore, underestimating air–sea heat exchange could lead to an overestimation of the inverse kinetic energy cascade, thereby distorting the overall energy budget in the KE region.
PubDate: 2025-06-21
- Response of Soil Moisture to Precipitation in the Source Region of the
Yellow River-
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Abstract: The source region of the Yellow River (SRYR), with its semi-humid to semi-arid climate, is crucial for understanding water resource dynamics. Precipitation is key for replenishing surface water and balancing the ecosystem’s water cycle. However, the soil moisture response to precipitation across climate zones and soil layers remains poorly understood due to limited long-term data. This study examines the response of soil moisture to precipitation at multiple time scales in the SRYR, using data from Maqu, Mado, Ngoring Lake sites, and the Maqu monitoring network (MMN), along with CN05.1 precipitation and GLEAM v3.8a soil moisture data. Results show that the semi-humid area requires more precipitation to trigger soil moisture responses compared to the semi-arid area in the SRYR. Surface soil at Maqu, MMN, Ngoring Lake, and Mado sites require at least 8.6, 8.4, 5.2, and 2.84 mm of precipitation, respectively, for effective replenishment. Significant responses to precipitation events were observed in soil layers at 40 cm and above in the semi-humid area, while at 20 cm and above in the semi-arid area. Precipitation volume is the primary factor influencing soil moisture, affecting both the increment and time lag to maximum moisture. Precipitation intensity and pre-rain moisture have no direct effect. In the central SRYR, accumulated precipitation has a greater impact. Root-zone soil moisture has a weaker correlation with precipitation compared to surface soil moisture but persists longer, responding for up to 10 days, while surface soil moisture responds more immediately but only lasts about 5 days.
PubDate: 2025-06-18
- Central Asian Compound Flooding in 2024 Contributed by Climate Warming and
Interannual Variability-
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Abstract: Extensive flooding swept across large areas of Central Asia, mainly over Kazakhstan and southwestern Russia, from late March to April 2024. It was reported to be the worst flooding in the area in the past 70 years and caused widespread devastation to society and infrastructure. However, the drivers of this record-breaking flood remain unexplored. Here, we show that the record-breaking floods were contributed by both long-term climate warming and interannual variability, with multiple climatic drivers at play across the synoptic to seasonal timescales. First, the heavy snowmelt in March 2024 was associated with above-normal preceding winter snow accumulation. Second, extreme rainfall was at a record-high during March 2024, in line with its increasing trend under climate warming. Third, the snowmelt and extreme rainfall in March were compounded by record-high soil moisture conditions in the preceding winter, which was a result of interannual variability and related to excessive winter rainfall over Central Asia. As climate warming continues, the interplay between the increasing trend of extreme rainfall, interannual variations in soil moisture pre-conditions, as well as shifting timing and magnitudes of spring snowmelt, will further increase and complicate spring flooding risks. This is a growing and widespread challenge for the mid- to high-latitude regions.
PubDate: 2025-06-14
- Estimation of Dynamic Characteristic Parameters for Long-Lived Tropical
Cyclone Freddy (2023) from Active/Passive Spaceborne Microwave Sensors-
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Abstract: Spaceborne microwave instruments possess the capability of day-and-night and all-weather measurements that can penetrate clouds and fog, and directly measure tropical cyclone (TC) ocean surface winds. In this study, we establish an effective methodology to estimate TC dynamic characteristic parameters (DCP), including the storm center location, intensity, radius of maximum wind (RMW) and wind structure, purely from TC ocean winds measured by multi-platform spaceborne microwave instruments. Combining measurements from active and passive sensors can provide long time series data for monitoring changes in storm DCP. Here, the evolution of the DCP for TC Freddy (2023), from its genesis to its landfall, is evaluated using data from synthetic aperture radars (SARs), as well as radiometer (RAD) and scatterometer (SCA) observations. Comparing the results to the best-track datasets for the longitudes and latitudes of the storm centers, we show that the root-mean-square errors (RMSEs) are 0.22° and 0.31°, respectively, both with a correlation of 0.99. For the detected intensity, the RMSEs are 6.8 m s−1 for SARs and 7.3 m s−1 for RADs. However, TC intensities measured by C-band SCAs are significantly underestimated, especially for wind speeds less than 50 m s−1. In terms of RMW and wind radii, the SARs, RADs and SCAs demonstrate good accuracy and applicability. Our investigation emphasizes the crucial role played by spaceborne microwave instruments in the study of TCs. This is helpful in monitoring, and in the future, will help improve the forecasting of TC intensities and their characteristic structures.
PubDate: 2025-06-13
- QBO Disruption–like Events in the China Meteorological
Administration Climate Model-
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Abstract: As a prominent mode of variability in the tropical stratosphere on the interannual timescale, the Quasi-Biennial Oscillation (QBO) can significantly influence global atmospheric circulation and weather patterns. This study explores the dynamic processes of QBO disruptions using the integrated climate model of the China Meteorological Administration (CMA) by nudging the tropical zonal winds toward observations. A comparative analysis with ERA5 reanalysis data shows that the nudged runs accurately replicate the general characteristics of the QBO, including the alternating QBO wind regimes and QBO disruption events. The evolution of the QBO winds is diagnosed using empirical orthogonal function and root-mean-square difference analyses, and the rarity of the disruption events is confirmed in the CMA model. Different aspects of the QBO disruptions and the relevant dynamics are present in the model. Firstly, the momentum budget analysis highlights the crucial roles of extratropical Rossby waves and non-orographic gravity waves in the transition from westerly to easterly winds during a disruption. Secondly, Kelvin waves and non-orographic gravity waves explain much of the transition from easterly to westerly winds near 40 hPa. Thirdly, the positive tendency from enhanced vertical advection further accelerates westerly momentum development via secondary meridional circulation. These findings underscore the importance of nudging techniques in understanding QBO dynamics, which provides valuable insights for future climate model improvements toward better forecasting QBO-related climate variability. Notably, due to model limitations, no QBO disruptions were simulated in the free-run experiments.
PubDate: 2025-06-13
- Sub-monthly Processes Contribute Significantly to CO2 Uptake in
the South China Sea-
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Abstract: Estimating the multi-year average air–sea CO2 flux over a large area usually involves the use of monthly mean variables from the atmosphere and ocean. Ignoring sub-monthly processes will blur the oceanic carbon cycle, especially when the synoptic and sub-seasonal scale processes are significant, like in the South China Sea (SCS). Based on an empirical relationship between the partial pressure of CO2 in water and the sea surface temperature (SST), we recalculated the air–sea CO2 flux of the SCS with daily products of atmospheric reanalysis and SST. Our results show that the sub-monthly process contributes 10% of the total CO2 flux of the SCS and can even alter the sign of the CO2 flux in the spring. In the near-surface coupling process, intramonthly variations in surface winds play the dominant role, except in regions with significant ocean eddies. The co-spectrum analysis of SST and wind speed reveals the most essential oscillation of >20 days. Therefore, a product of the sea surface environment for 10-day intervals can better estimate the air–sea CO2 flux over the SCS than monthly data.
PubDate: 2025-06-13
- Ocean Response for a Typical Leftward-Biased Cold Wake Induced by
Hurricane Jova (2005) in the Northeast Pacific-
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Abstract: In the Northern Hemisphere, cold wakes induced by tropical cyclones (TCs) are generally biased to the right of the storm track. However, a recent study found that a non-negligible proportion of cold wakes is actually leftward-biased. To further reveal the underlying physical mechanisms, the three-dimensional dynamic processes for the typical leftward cold wake of Hurricane Jova (2005) are investigated through a sequence of numerical simulations. Results reveal that the vertical advection in response to Jova (2005) is biased to the left of its track in the upper layer. In cooperation with the heterogenous ambient oceanic temperature stratification, the rightward vertical mixing is suppressed while the leftward feature of vertical advection is further intensified, which effectively promotes the formation of leftward cold wake. Additionally, the currents induced by Jova (2005) drive colder (warmer) water to the left (right) when coupled with background horizontal temperature gradients and then strengthen the leftward distribution of the temperature anomaly. These conclusions are substantiated by the control simulation, as the upper-layer temperature anomaly is restored to rightward disposition with homogeneous initial thermal structures. Based on three groups of sensitivity experiments, the leftward pattern of upwelling is found to be inextricably accompanied by the curl of wind stress caused by the movement of TCs. With the increase in translation speed from the stationary state, the symmetric structure of vertical velocity is gradually distorted to be leftward. Furthermore, the leftward bias distance of the upwelling center in the upper layer positively correlates with the radius of maximum wind, indicating that the wind structure can significantly influences the oceanic responses to TCs.
PubDate: 2025-06-11
- Interactions of the Background State and Eddies in Shaping Aleutian Low
Variations-
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Abstract: The Aleutian Low (AL) is a dominant feature of the mean circulation in the North Pacific during the winter season. The background stationary wave, air-sea interaction, and transient eddies over the North Pacific exert distinct impacts on the interannual variations of the AL intensity and position. In this study, we adopt the quasi-geostrophic geopotential tendency equation to investigate the roles of various physical processes in the maintenance and interannual variations of this system. The results show that absolute vorticity advection plays the most important role in the formation and maintenance of AL intensity, while high-frequency transient eddies contribute most to the meridional and zonal shifts of the AL. The high-frequency transient eddy vorticity forcing affects the AL through the barotropic energy conversion process, and, in turn, the AL enhances the high-frequency transient eddies through the baroclinic energy conversion process, forming a positive feedback. The associated high-frequency eddy kinetic energy anomalies exhibit an eastward movement toward the east coast of North America in the years of an intensified AL, which explains why a strengthened AL is often accompanied by an eastward movement. Furthermore, the energy conversion terms of high-frequency transient eddies are mostly located over the extratropical eastern North Pacific, leading to asymmetric features in the zonal movement of the AL.
PubDate: 2025-06-11
- Global Carbon Monoxide Column Derived from HIRAS-II/FY-3F Satellite
Observations-
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Abstract: The Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) onboard China’s FungYun (FY)-3F meteorological satellite was launched in August 2023. This study presents the first attempt to retrieve the global carbon monoxide (CO) column from HIRAS-II/FY-3F spectra based on a newly established full-physics algorithm. The CO global columns derived from the HIRAS-II/FY-3F satellite are compared to measurements from the Infrared Atmospheric Sounding Interferometer (IASI) onboard Europe’s MetopB satellite, as both satellites have the same spectral range with a similar overpass time. The correlation coefficient between the IASI/Metop-B and HIRAS-II/FY-3F CO retrievals is about 0.8. The HIRAS-II/FY-3F satellite can capture well the regions with high CO values, e.g., Africa, North America, and East Asia. The relative difference in the CO global column between HIRAS-II and IASI is 1.2±13.7(1σ)%, which is within their combined retrieval uncertainty. The CO plumes from the fire emissions in North America between 18 and 23 July 2024 were observed by the HIRAS-II/FY-3F satellite and consistent with the CAMS (Copernicus Atmosphere Monitoring Service) model simulations. Our results show that the HIRAS-II/FY-3F spectra are of good enough quality to provide quantitative observations of global CO column remote sensing observations.
PubDate: 2025-06-10
- Environmental Features of Heavy Precipitation Under Favorable Synoptic
Patterns: A Lesson from the 2021 Henan Extreme Precipitation Event-
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Abstract: In July 2021, a catastrophic extreme precipitation (EP) event occurred in Henan Province, China, resulting in considerable human and economic losses. The synoptic pattern during this event is distinctive, characterized by the presence of two typhoons and substantial water transport into Henan. However, a favorable synoptic pattern only does not guarantee the occurrence of heavy precipitation in Henan. This study investigates the key environmental features critical for EP under similar synoptic patterns to the 2021 Henan extreme event. It is found that cold clouds are better aggregated on EP days, accompanied by beneficial environment features like enhanced moisture conditions, stronger updrafts, and greater atmospheric instability. The temporal evolution of these environmental features shows a leading signal by one to three days. These results suggest the importance of combining the synoptic pattern and environmental features in the forecasting of heavy precipitation events.
PubDate: 2025-06-10
- Arctic Ocean Dynamical Downscaling Data for Understanding Past and Future
Climate Change-
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Abstract: The Arctic is one of Earth’s regions highly susceptible to climate change. However, in situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and current climate models exhibit notable biases in Arctic Ocean simulations. Here, we present an Arctic Ocean dynamical downscaling dataset, obtained from the global ocean-sea ice model FESOM2 with a regionally refined horizonal resolution of 4.5 km in the Arctic region, which is driven by bias-corrected surface forcings derived from a climate model. The dataset includes 115 years (1900–2014) of historical simulations and two 86-year future projection simulations (2015–2100) for the SSP2-4.5 and SSP5-8.5 scenarios. The historical simulations demonstrate substantially reduced biases in temperature, salinity and sea-ice thickness compared to CMIP6 climate models. Common biases in the representation of the Atlantic Water layer found in climate model simulations are also markedly reduced in the dataset. Serving as a crucial long-term data source for climate change assessments and scientific research for the Arctic Ocean, this dataset provides valuable information for the scientific community.
PubDate: 2025-06-10
- Macro- and Microphysical Characteristics of Freezing Rain and Their
Impacts on Wire Icing Mechanisms in the Southwestern Mountainous Areas of
China-
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Abstract: Based on comprehensive observations of 20 wire icing events during winter from 2019 to 2021, we investigated the characteristics of the icing properties, the atmospheric boundary layer structure, the raindrop size distribution, and their associated effects on the ice accretion mechanism in the mountainous region of Southwest China. The maximum ice weight was positively correlated with the duration of ice accretion in the mountainous area. The duration of precipitation accounted for less than 20% of the icing period in the mountainous area, with solid-phase hydrometeors being predominant. Icing events, dominated by freezing rain (FR) and mixed rain–graupel (more than 70%), were characterized by glaze or high-density mixed icing. The relationship between the melting energy and refreezing energy reflected the distribution characteristics of the proportion of FR under mixed-phase precipitation. The intensity of the warm layer and the dominant precipitation phase significantly affected the variations in the microphysical properties of FR. The melting of large dry snowflakes significantly contributed to FR in the mountainous areas, resulting in smaller generalized intercepts and larger mass-weighted mean diameters in the presence of a stronger warm layer. Under a weaker warm layer, the value of the mass-weighted mean diameter was significantly smaller because of the inability of large solid particles to melt. Finally, FR in the mountainous area dominated the ice weight during the rapid ice accumulation period. A numerical simulation of FR icing on wires effectively revealed the evolution of disaster-causing icing in mountainous areas.
PubDate: 2025-06-10
- Global Ensemble Weather Prediction from a Deep Learning–Based Model
(Pangu-Weather) with the Initial Condition Perturbations of CMA-GEPS-
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Abstract: Pangu-Weather (PGW), trained with deep learning-based methods (DL-based model), shows significant potential for global medium-range weather forecasting. However, the interpretability and trustworthiness of global medium-range DL-based models raise many concerns. This study uses the singular vector (SV) initial condition (IC) perturbations of the China Meteorological Administration’s Global Ensemble Prediction System (CMA-GEPS) as inputs of PGW for global ensemble prediction (PGW-GEPS) to investigate the ensemble forecast sensitivity of DL-based models to the IC errors. Meanwhile, the CMA-GEPS forecasts serve as benchmarks for comparison and verification. The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments. The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts. Meanwhile, PGW-GEPS is sensitive to the SV IC perturbations. Specifically, PGW-GEPS can generate realistic ensemble spread beyond the sub-synoptic scale (wavenumbers ⩽ 64) with SV IC perturbations. However, PGW’s kinetic energy is significantly reduced at the sub-synoptic scale, leading to error growth behavior inconsistent with CMA-GEPS at that scale. Thus, this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions. In terms of the global medium-range ensemble prediction performance, the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations. That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.
PubDate: 2025-06-10
- Impact of Spring Barents Sea Ice on Summer Tibetan Plateau Precipitation
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Abstract: The spring (April–May–June) Barents Sea ice has been proven to affect the summer surface air temperature over the Tibetan Plateau (TP). However, its impact on summer (June–July–August) TP precipitation, a crucial climate component, remains unexplored. We investigate the physical linkage between spring Barents Sea ice and subsequent summer TP precipitation from 1979 to 2018. Our results indicate that above-normal spring Barents Sea ice leads to excessive summer TP precipitation, and vice versa. During spring, more Barents Sea ice induces remarkable cooling and subsidence over there and surrounding areas. The cooling over the Barents Sea can persist into summer, triggering a meridional wave-like pattern along the longitude of 60°E and, in turn, an anomalous atmospheric subsidence over the Caspian Sea and the eastern region adjacent to it. This alters 200 hPa convergence and modulates the Silk Road pattern (SRP). As a result, cyclonic anomalies form to the west of the TP, which enhance moisture transport toward the TP and increase its precipitation during summer. Numerical experiments reproduce these physical processes and further support our conclusions.
PubDate: 2025-05-31
- Radiosonde Measurements and Polar WRF Simulations of Low-Level Wind Jets
in the Amundsen Sea Embayment, West Antarctica-
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Abstract: We show that low-level jets (LLJs) occurred in 11 out of 22 radiosonde profiles in late austral summer over the coastal region of the Amundsen Sea Embayment, with ten of the LLJs directed offshore. The LLJs had core speeds from 9 to 32 m s−1, jet core heights from 80 to 800 m, and were associated with strong, low-level temperature inversions. Seven of the observed offshore LLJs were reasonably simulated by the polar-optimized Weather Research and Forecasting (Polar WRF) model, with output from the model subsequently used to elucidate their generation mechanisms. This study shows that one of the offshore LLJs simulated by the Polar WRF was caused by katabatic winds, while the remaining six were caused by the enhancement of katabatic winds by synoptic forcing in response to a low-pressure system over the Bellingshausen Sea, i.e., the offshore wind component associated with this system plays a crucial role in the enhancement of the katabatic LLJ. Examination of the Polar WRF output further shows that the LLJs extended over large areas of the Amundsen Sea Embayment, resulting in substantially enhanced near-surface wind speeds over both the Thwaites and Pine Island ice shelves, as well as the open ocean over the continental shelf. The wind-driven forcing associated with the LLJs could perhaps have important impacts on the redistribution of snow over the ice shelves significantly, as well as to affecting sea-ice and ocean circulation variability, including the transport of relatively warm water over the continental shelf to the ice shelf cavities and extension basal melting.
PubDate: 2025-05-28
- Impact of the Sequential Bias Correction Scheme on the CMA-MESO Numerical
Weather Prediction Model-
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Abstract: Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed. An online bias correction scheme called the sequential bias correction scheme (SBCS), was developed using the 6 h average bias to correct the systematic bias during model integration. The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale (CMA-MESO) numerical weather prediction (NWP) model to reduce the systematic bias and to improve the data assimilation and forecast results through this method. The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study. Four-week sequential data assimilation and forecast experiments, driven by rapid update and cycling (RUC), were conducted for the period from 2–29 May 2022. In terms of the characteristics of systematic bias, both the background and analysis show diurnal bias, and these large biases are affected by complex underlying surfaces (e.g., oceans, coasts, and mountains). After the application of the SBCS, the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields. In addition, the SBCS can reduce forecast errors and improve forecast results, especially for surface variables. The above results indicate that this scheme has good prospects for high-resolution regional NWP models.
PubDate: 2025-05-24
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