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Drought monitoring is crucial for various sectors, and soil moisture data play a pivotal role, especially in agricultural contexts. This study focuses on the recent CYGNSS Level 3 soil moisture data derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS), notable for its wide coverage and rapid revisit times, yet underexplored in drought research. Spanning from 1 January 2018 to 31 December 2022, this research analyzed daily CYGNSS soil moisture data, comparing them with the ERA5, SMAP, and GLDAS-NOAH datasets. It was found that the average and standard deviation (std) of CYGNSS soil moisture exhibited spatial patterns largely similar to other datasets, although some regions showed discrepancies (std differences reached up to 0.05 in some regions). The correlation coefficients and RMSE values between CYGNSS and other datasets depended on climate and land cover types. Four drought indicators from different soil moisture datasets were compared with the improved monthly Standardized Precipitation Evapotranspiration Index (SPEI). The drought indicators based on CYGNSS data demonstrate the capacity to describe drought extent and intensity. The correlation coefficients between certain drought indicators obtained from CYGNSS and SPEI reached 0.27 for drought percentage and 0.16 for drought intensity. Further investigations with selected extreme drought cases revealed that the indicator from CYGNSS data is relatively weak, influenced by the selected regions, times, and drought indicators. The results of this study provide insights into the potential application of CYGNSS soil moisture data in drought monitoring, offering a foundation for future research and practical implementation with current and future improved products. [ABSTRACT FROM AUTHOR]

Variations in the sea surface temperature (SST) field in both the North Pacific (represented by the Victoria mode [VM]) and the South Pacific (represented by the South Pacific quadrapole [SPQ] mode) are related to the state of the El Niño–Southern Oscillation (ENSO) three seasons later. Here with the aid of observational data and numerical experiments, we demonstrate that both VM and SPQ SST forcing can influence the development of ENSO events through a similar air‐sea coupling mechanism. By comparing ENSO amplitudes induced by the VM and SPQ, as well as the percentages of strong ENSO events followed by the VM and SPQ events, we find that the VM and SPQ make comparable contributions and therefore have similar levels of importance to ENSO. Additional analysis indicates that although VM or SPQ SST forcing alone may serve as a good predictor for ENSO events, it is more effective to consider their combined influence. A prediction model based on both VM and SPQ indices is developed, which is capable of yielding skillful forecasts for ENSO at lead times of three seasons. Key Points: The extratropical North and South Pacific Oceans make comparable contributions to the development of ENSOThe extratropical North and South Pacific precursor signals may be used together to better predict the ENSO events [ABSTRACT FROM AUTHOR]

Nonlinear local Lyapunov vectors (NLLVs) are the nonlinear extension of the Lyapunov vectors (LVs) based on linear error growth theory. As a development of bred vectors (BVs), NLLVs retain the time-saving, simply-applied and flow-dependent advantages of BVs. However, unlike BVs, NLLVs correspond not only to the leading LV but also to other orthogonal LVs. In this paper, NLLVs are applied to the Zebiak-Cane (ZC) coupled model. First, using the analysis data from the ensemble Kalman filter, we explore the effect of the parameters of the breeding process on calculating the NLLVs. It is found that the statistical properties of NLLVs are not very sensitive to the breeding parameters. However, the higher NLLVs (i.e., excluding NLLV1) show temporal randomness. Then, we study the characteristics of the spatial structures and growth rates of different NLLVs. The different NLLVs each have a certain probability of being the fastest error growth direction and together construct the error growth subspace of the ZC model. Compared with BVs, the NLLVs have some advantages in terms of the relationship between the generated error growth subspace and the analysis errors. The NLLVs also have higher local dimensionality than the BVs. NLLVs, as initial ensemble perturbations, are applied to the ensemble prediction of ENSO in a perfect environment. Compared with the results obtained using ensembles employing the random perturbation technique and the BV method, the present results demonstrate the advantages of using the NLLV method in ensemble forecasts. [ABSTRACT FROM AUTHOR]