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July is the rainy peak month of central China, with a large interannual variation of local precipitation often causing serious droughts and floods. The seasonal prediction of the central China July precipitation (CCJP) is an important but still challenging task. Here, we suggest several robust seasonal predictors for the CCJP, including the preceding winter intensity of El Niño‐South Oscillation (ENSO), the winter‐to‐spring decaying rate of ENSO signals in the central Pacific, as well as the spring tropical and subpolar North Atlantic sea surface temperature anomalies. A physics‐based empirical model is then developed to predict the CCJP by using the principal component regression of the aforementioned seasonal predictors. In our statistical model, the seasonal prediction skill of the CCJP is high, with the cross‐validated reforecast skill at 0.81 during 1993–2021. This suggests a skillful seasonal prediction of the CCJP, with potentially enormous benefits for the local society and economy. Plain Language Summary July contributes about 20% of annual precipitation for the densely populated central China, which could exert tremendous socio‐economic impacts over the region, including agriculture, water resources, food security, ecosystems, disaster mitigation, infrastructure construction, human health, and so on. Thus, how to skillfully predict the interannual variation of the central China July precipitation (CCJP) is a widespread scientific and socio‐economic concern. This work establishes a statistical model for the seasonal prediction of the CCJP by combining the physical precursor factors that drive the interannual variation of the CCJP. Our physics‐based empirical model can well predict the CCJP at one‐season lead, with the cross‐validated reforecast skill at 0.81 during 1993–2021. This provides a substantial skill of the seasonal prediction of the CCJP and is of potentially great importance to the regional agrarian‐based livelihood of hundreds of millions of people. Key Points The Pacific and North Atlantic sea surface temperature precursors are suggested as robust seasonal predictors of the central China July precipitation (CCJP) A physics‐based empirical prediction model of the CCJP is developed based on the principal component regression of the seasonal predictors The prediction skill of the CCJP in our statistical model is high, with a cross‐validated reforecast skill at 0.81 during 1993–2021

Bedload flux estimation from reach‐averaged hydraulic conditions, while tractable and generally reliable over long integration periods, struggles to capture the intrinsic variability of transport in turbulent flow and the strong influence of local sediment size and morphological heterogeneity. Here we suggest an empirical equation to simplify the relationship between seismic power spectral density (PSD) and bedload flux relative to a full physics‐based seismic model. We posit that adding seismic PSD as a predictor to hydraulics‐based bedload equations improves bedload flux predictions by accounting for short‐ and medium‐term flux variations independent of flow conditions. We present a new calibrated empirical equation combining seismic PSD and excess shear stress to predict bedload flux at high temporal resolution (minute‐scale). The calibrated coefficients for the shear contribution are consistent with existing hydraulics‐based equations (e.g., Meyer‐Peter and Müller) that have been calibrated across a broad range of channels, suggesting that the values for the seismic parameter might also be broadly applicable across channels. In comparison to field data from a sandy‐gravel‐bed alluvial river in New Mexico, USA, the locally‐trained equation reduces scatter in bedload flux predictions relative to methods solely using either seismic PSD or shear stress. We further validate the equation with independent flow events from the same river and from a separate gravel bed channel, the Nahal Eshtemoa in Israel. Notably, the seismic‐hydraulic equation was able to be calibrated on low‐transport data and reliably predict high‐transport data. Likewise, the seismic‐hydraulic equation reduced apparent overestimations of bedload flux by the hydraulics‐based equation during high‐shear conditions.

Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models, with a root mean square error (RMSE) of 1.41 m and a regression coefficient (R2) of 0.96 for the test data.

Hansen Solubility Parameters (HSPs) are widely used as a tool in solubility studies. Given the variety of existent approaches to predict these parameters, this investigation focused on estimating the HSPs of a set of Natural Deep Eutectic Systems (NADES), using empirical (EM) and semi-empirical models (SEM), and then understanding their differences/similarities. Although these theoretical models are designed and recommended mostly for simple molecules or simple solutions, they are still being used in eutectic systems studies, mainly empirical ones. Thus, a preliminary test was conducted with a set of conventional solvents, in which their experimental values of HSPs are known. Besides the confirmation of the EM as the most suitable for these kinds of regular solvents, the results found also showed a very similar behaviour to what was observed in NADES, i.e., in terms of suggesting the EM and SEM with the highest/lowest similarity. Furthermore, it was concluded that although there is a large discrepancy between the estimated values of the hydrogen bond parameter, especially for systems with a higher polar character, there is still a good similarity for the other parameters. In fact, it was observed that, when combining the semi-empirical models, it was possible to obtain a value of the hydrogen bond parameter more similar to the empirical ones.

Extreme precipitation events have severe impacts on human activity and natural environment, but prediction of extreme precipitation events remains a considerable challenge. The present study aims to explore the sources of predictability and to estimate the predictability of the summer extreme precipitation days (EPDs) over eastern China. Based on the region- and season-dependent variability of EPDs, all stations over eastern China are divided into two domains: South China (SC) and northern China (NC). Two domain-averaged EPDs indices during their local high EPDs seasons (May–June for SC and July–August for NC) are therefore defined. The simultaneous lower boundary anomalies associated with each EPDs index are examined, and we find: (a) the increased EPDs over SC are related to a rapid decaying El Nino and controlled by Philippine Sea anticyclone anomalies in May–June; (b) the increased EPDs over NC are accompanied by a developing La Nina and anomalous zonal sea level pressure contrast between the western North Pacific subtropical high and East Asian low in July–August. Tracking back the origins of these boundary anomalies, one or two physically meaningful predictors are detected for each regional EPDs index. The causative relationships between the predictors and the corresponding EPDs over each region are discussed using lead-lag correlation analyses. Using these selected predictors, a set of Physics-based Empirical models is derived. The 13-year (2001–2013) independent forecast shows significant temporal correlation skills of 0.60 and 0.74 for the EPDs index of SC and NC, respectively, providing an estimation of the predictability for summer EPDs over eastern China.

Summer surface air temperature (SAT) variability over Northern Hemisphere (NH) continents can profoundly impact human society, yet its seasonal prediction remains challenging, partly due to the limited prediction skill of dynamical models, especially over extratropical and high-latitude areas. Previous research has defined five indices associated with different atmospheric circulation patterns, which have important contributions to variations of summer SAT. This study further establishes a physically based empirical model (P–E model) using the Bayesian dynamic linear model method for the prediction of the indices, and uses the predicted indices to reconstruct the summer SAT anomaly field. Results show that the P–E model can reasonably well predict the five indices during 1950–2021. Combining this with the linear trend, the total summer SAT anomaly is also reconstructed. The high cross-validated hindcast skill for the period of 1950–2021 and independent forecast skill of 2022 indicate that the summer SAT over NH continents can be reasonably predicted by the P–E model.

The precise description of the topside ionosphere using an ionospheric empirical model has always been a work in progress. The NeQuick topside model is greatly enhanced by adopting radio occultation data from the FORMOSAT-7/COSMIC-2 constellation. The topside scale height H formulation in the NeQuick model is simplified into a linear combination of an empirically deduced parameter H 0 and a gradient parameter g . The two-dimensional grid maps for the H 0 and g parameters are generated as a function of the foF 2 and hmF 2 parameters. Corrected H 0 and g values can be interpolated easily from two grid maps, allowing a more accurate description of the topside ionosphere than the original NeQuick model. The improved NeQuick model (namely NeQuick_GRID model) is statistically validated by comparing it to Total Electron Content (TEC) integrated from COSMIC-2 electron density profiles and space-borne TEC derived from onboard Global Navigation Satellite System observations, respectively. The results show that the NeQuick_GRID model can reduce relative errors by 38% approximately when compared to the integrated TEC from COSMIC profiles and by 15% approximately when compared to the space-borne TEC. Furthermore, a long-term statistical analysis during years of both high and low solar activities reveals that grid maps of the scale factor H 0 and the gradient parameter g have very similar features, allowing rapid and efficient acquisition of high-precision electron density during different solar activity.

This study describes a semi-empirical model developed to estimate volumetric soil moisture ( v ϑ) in bare soils during the dry season (March–May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient (σ° dB) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India. The backscatter coefficients 0 VV σ and 0 VH σ were extracted from SAR images at 62 geo-referenced locations where ground sampling and volumetric soil moisture were measured at a 10 cm (0–10 cm) depth using a soil core sampler and a standard gravimetric method during the dry months (March–May) of 2017 and 2018. A linear equation was proposed by combining 0 VV σ and 0 VH σ to estimate soil moisture. Both localized and generalized linear models were derived. Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization(VH) separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized models were derived by combining the two years (2017 and 2018) for each polarimetric channel; and three more models were derived for the linear combination of 0 VV σ and 0 VH σ . The above set of equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and 2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and generalized models were compared with in situ data. Both kind of models revealed that the linear combination of 0 VV σ + 0 VH σ showed a significantly higher R2 than the individual polarimetric channels.

Generalised statements about the state of fire science are often used to provide a simplified context for new work. This paper explores the validity of five frequently repeated statements regarding empirical and physical models for predicting wildland fire behaviour. For empirical models, these include statements that they: (1) work well over the range of their original data; and (2) are not appropriate for and should not be applied to conditions outside the range of the original data. For physical models, common statements include that they: (3) provide insight into the mechanisms that drive wildland fire spread and other aspects of fire behaviour; (4) give a better understanding of how fuel treatments modify fire behaviour; and (5) can be used to derive simplified models to predict fire behaviour operationally. The first statement was judged to be true only under certain conditions, whereas the second was shown not to be necessarily correct if valid data and appropriate modelling forms are used. Statements three through five, although theoretically valid, were considered not to be true given the current state of knowledge regarding fundamental wildland fire processes.

This work reports the development of the first version of the E‐region Prompt Radio Occultation Based Electron Density (E‐PROBED) Model. This is an empirical model of E‐region electron density (Ne) between 90 and 120 km developed using radio occultation measurements from the COSMIC‐1 mission. This first version captures more than 80% of the observed variability in monthly‐mean latitude‐local time‐altitude E‐region Ne profiles but it does not account for longitudinal variability at constant local‐time. This work also reports a validation of E‐PROBED simulations through comparisons with ionosondes and incoherent scatter radar (ISR) E‐region Ne profiles. E‐PROBED generally agrees with these ground‐based observations during day‐time. During night‐time, there is a large disparity between E‐PROBED and ISR values. Finally, this work compares E‐PROBED with E‐region Ne simulated by the International Reference Ionosphere (IRI) and the Specified Dynamics—Whole Atmosphere Community Climate Model with Ionosphere/Thermosphere eXtension (SD‐WACCM‐X). One of the main differences amongst these models is on the simulation of variabilities that cannot be attributed to photoionization. IRI barely simulates any variability not driven by photoionization. Both E‐PROBED and SD‐WACCM‐X simulates variability not driven by photoionization. Another main difference is in the absolute magnitude of night‐time E‐region Ne values. Both IRI and SD‐WACCM‐X are substantially lower than E‐PROBED. This work first concludes that E‐PROBED can conveniently provide E‐region Ne latitude—local time variabilities and structures that COSMIC‐1 observes. This work also concludes that E‐region Ne have significant non‐photoionization driven variabilities.