More Like this

1. Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments

   https://doi.org/10.1175/WAF-D-22-0156.1  

   Zhang, Yunji; Chen, Xingchao; Bell, Michael M. (April 2023, Weather and Forecasting)

   Abstract The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future. Significance Statement During the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well. 

   more » « less
2. Rethinking Indian monsoon rainfall prediction in the context of recent global warming

   https://doi.org/10.1038/ncomms8154  

   Wang, B; Xioang, B; L, i J; Webster, PJ; Rajeevan, MN; Liu, . J; Ha, K-H (January 2015, Nature communications)

   Prediction of Indian summer monsoon rainfall (ISMR) is at the heart of tropical climate prediction. Despite enormous progress having been made in predicting ISMR since 1886, the operational forecasts during recent decades (1989–2012) have little skill. Here we show, with both dynamical and physical–empirical models, that this recent failure is largely due to the models’ inability to capture new predictability sources emerging during recent global warming, that is, the development of the central-Pacific El Nino-Southern Oscillation (CP–ENSO), the rapid deepening of the Asian Low and the strengthening of North and South Pacific Highs during boreal spring. A physical–empirical model that captures these new predictors can produce an independent forecast skill of 0.51 for 1989–2012 and a 92-year retrospective forecast skill of 0.64 for 1921–2012. The recent low skills of the dynamical models are attributed to deficiencies in capturing the developing CP–ENSO and anomalous Asian Low. The results reveal a considerable gap between ISMR prediction skill and predictability. 

   more » « less
3. Intraseasonal variability of global land monsoon precipitation and its recent trend

   https://doi.org/10.1038/s41612-022-00253-7  

   Liu, Fei; Wang, Bin; Ouyang, Yu; Wang, Hui; Qiao, Shaobo; Chen, Guosen; Dong, Wenjie (December 2022, npj Climate and Atmospheric Science)

   Abstract Accurate prediction of global land monsoon rainfall on a sub-seasonal (2–8 weeks) time scale has become a worldwide demand. Current forecasts of weekly-mean rainfall in most monsoon regions, however, have limited skills beyond two weeks, calling for a more profound understanding of monsoon intraseasonal variability (ISV). We show that the high-frequency (HF; 8–20 days) ISV, crucial for the Week 2 and Week 3 predictions, accounts for about 53–70% of the total (8–70 days) ISV, generally dominating the sub-seasonal predictability of various land monsoons, while the low-frequency (LF; 20–70 days)’s contribution is comparable to HF only over Australia (AU; 47%), South Asia (SA; 43%), and South America (SAM; 40%). The leading modes of HFISVs in Northern Hemisphere (NH) monsoons primarily originate from different convectively coupled equatorial waves, while from mid-latitude wave trains for Southern Hemisphere (SH) monsoons and East Asian (EA) monsoon. The Madden-Julian Oscillation (MJO) directly regulates LFISVs in Asian-Australian monsoon and affects American and African monsoons by exciting Kelvin waves and mid-latitude teleconnections. During the past four decades, the HF (LF) ISVs have considerably intensified over Asian (Asian-Australian) monsoon but weakened over American (SAM) monsoon. Sub-seasonal to seasonal (S2S) prediction models exhibit higher sub-seasonal prediction skills over AU, SA, and SAM monsoons that have larger LFISV contributions than other monsoons. These results suggest an urgent need to improve the simulation of convectively coupled equatorial waves and two-way interactions between regional monsoon ISVs and mid-latitude processes and between MJO and regional monsoons, especially under the global warming scenarios. 

   more » « less
4. Evaluating Coupled Climate Model Parameterizations via Skill at Reproducing the Monsoon Intraseasonal Oscillation

   https://doi.org/10.1175/JCLI-D-21-0337.1  

   Orenstein, Patrick; Fox-Kemper, Baylor; Johnson, Leah; Li, Qing; Sane, Aakash (March 2022, Journal of Climate)

   Abstract Empirically generated indices are used to evaluate the skill of a global climate model in representing the monsoon intraseasonal oscillation (MISO). This work adapts the method of Suhas et al., an extended empirical orthogonal function (EEOF) analysis of daily rainfall data with the first orthogonal function indicating MISO strength and phase. This method is applied to observed rainfall and Community Earth System Model (CESM1.2) simulation results. Variants of the CESM1.2 including upper ocean parameterizations for Langmuir turbulence and submesoscale mixed layer eddy restratification are used together with the EEOF analysis to explore sensitivity of the MISO to global upper ocean process representations. The skill with which the model variants recreate the MISO strength and persistence is evaluated versus the observed MISO. While all model versions reproduce the northward rainfall propagation traditionally associated with the MISO, a version including both Langmuir turbulence and submesoscale restratification parameterizations provides the most accurate simulations of the time scale of MISO events. 

   more » « less
5. Case Studies of the Monsoon Onset Vortex and Early Season Monsoon Features Using the UFS Coupled Model

   https://doi.org/10.1175/WAF-D-25-0132.1  

   Dhavale, Shreyas; Aiyyer, Anantha; Stan, Cristiana (April 2026, Weather and Forecasting)

   Abstract The monsoon onset vortex (MOV) forms in the Arabian Sea during the onset phase of the Indian summer monsoon. The track and intensity of the MOV affect the monsoon rainfall during its onset phase, mainly over the west coast of India. This paper evaluates the reforecasts of MOV cases in the years 2011, 2014, and 2015 and their impacts on the monsoon rainfall in the Unified Forecast System (UFS) subseasonal to seasonal (S2S) prototypes (P5, P6, and P8). Out of the three MOV cases, the 2015 MOV is forecasted better across the UFS S2S prototypes. The possible factors influencing MOV forecasts in the UFS are analyzed through feature-based diagnostics. Our findings show that predicting the low-level Somali jet as well as the mesoscale-to-synoptic-scale evolution of deep convection is likely to result in more accurate MOV forecasts. The implications of MOV forecasts on the monsoon rainfall during the onset phase are discussed through these MOV cases. The UFS also captures the relationship between the Somali jet index and monsoon rainfall over India for the early–midpart of the monsoon (mid-June–mid-July). However, it underestimates the anomalous increase in rainfall corresponding to a stronger Somali jet. Overall, the UFS S2S prototypes P6 and P8 better forecast the MOV cases compared to P5. 

   more » « less