Abstract Land surface processes are strongly associated with heat waves (HWs). However, how the uncertainties in land owing to inaccurate physical parameters influence subseasonal HW predictions has rarely been explored. To examine impact of parameter errors on uncertainty predictions, five strong and long‐lasting events over middle lower reaches Yangtze River investigated. Based Weather Research Forecasting model, conditional nonlinear optimal perturbation related (CNOP‐P) approach is...

Financial time series forecasting plays a crucial role in predicting future market trends, pricing assets, and managing risks financial markets. This paper compares traditional methods, such as ARIMA, Exponential Smoothing, GARCH, with AI-driven techniques, including machine learning deep models, for forecasting. Traditional models are well-established effective stationary data, but they struggle non-linear relationships large datasets. In contrast, Random Forests, Long Short-Term Memory...

Precise identification of tree trunks contributes to the understanding urban green dynamics. Previous attempts develop trunk detection methods have faced limitations in respect precision and generalization due use hand-engineered features constraint single-species detection. In this study, we construct a new dataset considering object's strong diversity propose deep model detect segment salient or even branches scenes. Comprehensive experiments are performed evaluate our model. The presented...

Abstract. Human activities and climate change are important factors that affect grassland ecosystems. A new optimization approach, the approach of conditional nonlinear optimal perturbation (CNOP) related to initial parameter perturbations, is employed explore nonlinearly combined impacts human on a ecosystem using theoretical model. In our study, it assumed perturbations regarded as change, respectively. Numerical results indicate changes causing maximum effect in different under disparate...

Abstract Vegetation cover exerts a strong control on land‐atmosphere interactions. To quantify the relative effects of external forcing (climate change) versus internal (anthropogenic activity) recent vegetation change over Tibetan Plateau (TP), we apply an ecohydrological diagnostic framework, developed from earlier work. We compare during 1986–2015 based NDVI (Normalized Difference Index) data with changes in environmental conditions (European Centre for Medium‐Range Weather Forecasts...

Abstract Model parameter errors are one of the sources uncertainty when simulating or predicting evapotranspiration (ET) over Tibetan Plateau (TP). To enhance ET simulation ability and prediction skill TP, conditional nonlinear optimal perturbation (CNOP‐P) method is used to establish an ensemble (called CNOP‐PEP method) represent uncertainties arising due model parameters implement experiments. The one‐at‐a‐time (OAT) traditional stochastically perturbed parametrization (SPP) scheme also...

In this paper, we attempted to entend the application of conditional nonlinear optimal perturbation(CNOP) optimization parameters in land surface model. We used common model and data Tongyu station,which is a reference site CEOP semi-arid regions, three key (soil color, soil sand/lay proportion leaf area index) as be optimized. Two experiments are designed our work, namely single-parameter triple-parameter optimization. Notable improvements simulating sensible heat flux (SH), latent (LH),...

To provide scientific support for improvements in land surface modeling on the Tibetan Plateau (TP) by reducing uncertainties physical parameters of models, comprehensive uncertainty and sensitivity evaluations were performed simulation soil moisture (SSM). Five observational stations selected study. The conditional nonlinear optimal perturbation related to (CNOP-P) approach Common Land Model (CoLM) with 28 uncertain employed evaluate maximal simulated SSM. analysis indicated that parameter...

Abstract Initial errors and model parameter are two of the main factors that produce uncertainties in numerical simulations predictions. It is crucial to determine advance which these types should be reduced improve increase their prediction skill. In this study, a fundamental issue related studies predictability about terrestrial carbon cycle discussed. The relative importance initial causing uncertainty net primary production (NPP), part cycle, assessed. NPP predictions evaluated within...

Abstract Estimation of the Evapotranspiration (ET) over Tibetan Plateau (TP) still keeps uncertain due to inaccurate physical parameters and processes in numerical models. In this study, simulated ET is evaluated for 13 sites TP during period 2001–2017 using conditional nonlinear optimal perturbation related (CNOP‐P) approach. To find key processes, sensitivity analysis (SA) method based on CNOP‐P employed. The traditional SA (one‐at‐a‐time, OAT) also employed compare identification subset....

Abstract Understanding eastward‐propagating mechanisms of the Madden–Julian Oscillation (MJO) is great importance for subseasonal prediction extreme weather and climate worldwide. Using global satellite observations reanalysis data, this study unravels that dual combinations strong/weak westward‐ (ISOw) intraseasonal oscillation (ISOe) can shape diverse MJO propagations documented previously using clustering analysis. The dry ISOw signals from Central Pacific strengthen leading suppressed...