Prediction Of Summer Rainfall Research Articles

Analysis and Prediction of Summer Rainfall over Southwestern Utah

Abstract While many studies have examined intense rainfall and flash flooding during the North American Monsoon (NAM) in Arizona, Nevada, and New Mexico, less attention has focused on the NAMS’s extension into southwestern Utah. This study relates flash flood reports and Multi-Radar Multi-Sensor (MRMS) precipitation across southwestern Utah to atmospheric moisture content and instability analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model during the 2021–23 monsoon seasons. MRMS quantitative precipitation estimates over southwestern Utah during summer depend largely on the areal coverage from the KICX WSR-88D radar near Cedar City, UT. Those estimates are generally consistent with the limited number of precipitation gauge reports in the region except at extended distances from the radar. A strong relationship is evident between days with widespread precipitation and afternoons with above average precipitable water (PWAT) and convective available potential energy (CAPE) estimated from HRRR analyses across the region. Time-lagged ensembles of HRRR forecasts (initialization times from 03–06 UTC) that are 13–18 h prior to the afternoon period when convection is initiating (18–21 UTC) are useful for situational awareness of widespread precipitation events after adjusting for underprediction of afternoon CAPE. Improved skill is possible using random forest classification relying only on PWAT and CAPE to predict days experiencing excessive (upper quartile) precipitation. Such HRRR predictions may be useful for forecasters at the Salt Lake City National Weather Service Forecast Office to assist issuing flash flood potential statements for visitors to national parks and other recreational areas in the region.

Correction: A multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indices

Correction: A multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indices

A multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indices

A multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indices

Improving seasonal prediction of summer rainfall over southern China using the BCC_CSM1.1m model‐circulation increment‐based dynamic statistical technique

AbstractA model‐circulation increment‐based dynamic statistical technique (MIDST) is proposed in this paper to improve the prediction of summer rainfall over southern China (SC) where quite low prediction skills have been found in the Beijing Climate Center Climate System Model version 1.1 with a moderate resolution (BCC_CSM1.1m). The results show that BCC_CSM1.1m can hardly represent the variability of the summer rainfall anomaly and its year‐to‐year increment over SC, and the skillful predictions are mostly confined over the middle reaches of the Yangtze River. Using the dynamic model output and statistical method, the MIDST is established to capture the coupled modes between the year‐to‐year increments of the summer rainfall anomaly and the associated simultaneous three‐dimensional coupled air‐sea circulation predictors. The cross‐validation indicates that the prediction skills of the MIDST are evidently improved for both the summer rainfall increment prediction and summer rainfall anomaly prediction compared with the direct BCC_CSM1.1m prediction. The skillful prediction can persist for long forecast leads over most regions except southwestern China. As the major predictability source of seasonal prediction, the intense response to changes in the circulation related to the El Niño‐Southern Oscillation (ENSO) is well captured, and thus, the performance improvement of the MIDST is primarily due to its more realistic representation of the incremental circulation related to the ENSO.

Influence of ENSO on the ECMWF subseasonal prediction of summer rainfall over the Yangtze River

Influence of ENSO on the ECMWF subseasonal prediction of summer rainfall over the Yangtze River

Subseasonal Great Plains Rainfall via Remote Extratropical Teleconnections: Regional Application of Theory‐Guided Causal Networks

AbstractLong‐range U.S. summer rainfall prediction skill is low. Monsoon variability, especially over the West North Pacific Monsoon (WNPM) and/or East Asian Monsoon (EAM) region, can influence U.S. Great Plains hydroclimate variability via a forced Rossby wave response. Here, we explored subseasonal monsoon variability as a source of predictability for Great Plains rainfall. The boreal summer intraseasonal oscillation (BSISO) is related to Great Plains convection and Great Plains low‐level jet (LLJ) anomalies as well as a cross‐Pacific wave train. Using a causal effect network, we found that the time between BSISO‐related geopotential height anomalies and Great Plains rainfall anomalies is about 2 weeks; therefore, BSISO convection may be a valuable forecast of opportunity for subseasonal prediction of Great Plains convection anomalies. More specifically, causal link patterns/maps revealed that the above‐normal weekly EAM rainfall, rather than WNPM rainfall or general geopotential height activity over the East Asia, was causally linked to Great Plains LLJ strengthening and active Great Plains convection the following week.

A hybrid statistical-dynamical prediction model for summer precipitation in northwestern China based on NCEP CFSv2

As a typical arid/semi-arid area, summer rainfall in northwestern China (NWC) is critical for the ecological environment there. The NWC summer rainfall prediction using statistical and dynamical models shows very poor performance. The dynamical model NCEP CFSv2 shows very low prediction skill in NWC summer precipitation. NWC precipitation index (NWC PI) is defined as the regional mean summer precipitation over NWC. This study first investigates the soil moisture and SST (sea surface temperature) signals in the preceding months, and CFSv2 April predicted summer large-scale circulation anomalies significantly related with NWC PI during 1982–2016 (2011 missing). The April soil moisture in the upstream central Asia, and SST in the preceding winter over tropical Pacific, Atlantic and Indian Ocean are identified as predictor candidates in the observation. Besides, six April predicted large-scale circulation factors in CFSv2 are also hired. The potential model predictors are selected generally based on three criteria: where CFSv2 has significant prediction skill, and NWC PI is significantly correlated with the variable in both the observation and its counterpart in CFSv2 prediction. Hybrid models are then constructed based on different predictor combinations. The leave-one-out cross validation illustrates that the hybrid model using three predictors (April soil moisture in the upstream central Asia, April predicted zonal wind over the Maritime Continent, April predicted SLP over the Mediterranean Sea) presents best performance, with anomalies correlation coefficient–ACC of 0.65, percentage of the same sign–PERC 76.5%, relative error–RE 6.97%, and root mean square error–RMSE 0.344 mm/day for NWC PI. The April soil moisture in the upstream central Asia is linked with the summer water vapor flux over NWC, and April predicted zonal wind over the Maritime Continent is related with the large-scale circulation over NWC, while caution must be taken when April predicted SLP over the Mediterranean Sea is used as predictor of NWC PI.

Multi-predictor ensembles improving seasonal prediction of summer rainfall over the Bohai Sea Rim based on statistical downscaling of BCC_CSM1.1 m

A model-circulation-based statistical downscaling (MCSD) method and its multi-predictor ensemble (MPE) schemes are proposed in this study to improve summer (June–July-August) rainfall prediction over the Bohai Sea Rim (BSR) area in the northern China where quite low prediction skills were found in Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). This MCSD method, using both the empirical orthogonal function and singular value decomposition, can largely eliminate noise within variables and extract leading coupled patterns between the observed BSR summer rainfall and simultaneously predicted large-scale atmospheric circulation to significantly add prediction skill and spatial resolution. Three circulation anomaly predictors, the 500-hPa geopotential height (H500), 200-hPa zonal wind (U200) and 850-hPa meridional wind, are selected to implement statistical downscaling, and their MPE means are compared for verifications. Cross validations show considerable improvements of the MCSD with the MPE schemes, especially H500 and its ensemble mean with U200 (H5U2), where almost all temporal correlation coefficients between the predictions and observation are significantly positive over most BSR area, and particularly, spatial correlation coefficients reach 0.24 and 0.19 from −0.05 of the original model and the anomaly sign consistency rates reach 58.2% and 58.4% from 48.4%. In MCSD, to deal with the deficiency of underestimated rainfall anomaly, an intensity inflation for the downscaled rainfall prediction is carried out, which makes temporal correlation coefficients for the area-averaged H500 and H5U2 predictions improved from about 0.54 to 0.7. Overall, not only the tendency and spatial distribution but also magnitude accuracy of summer rainfall prediction over BSR can be significantly improved by the MCSD and its MPEs, providing a potentially effective tool for the mid-latitude seasonal prediction of summer rainfall usually showing low skills in dynamical models.

Impact of Solar Activity on Snow Cover Variation Over the Tibetan Plateau and Linkage to the Summer Precipitation in China

Solar activity is one of the main external forcing factors driving the Earth’s climate system to change. The snow cover over the Tibetan Plateau is an important physical factor affecting the East Asian climate. At present, insufficient research on the connection between solar activity and snow cover over the Tibetan Plateau has been carried out. Using Solar Radio Flux (SRF), Solar Sunspot Number (SSN), and Total Solar Irradiance (TSI) data, this paper calculated the correlation coefficients with snow indices over the Tibetan Plateau, such as winter and spring snow depth (WSD/SSD) and snow day number (WSDN/SSDN). These snow indices are obtained from the daily gauge snow data in the Tibetan Plateau. Through correlation analyses, it is found that there are significant synchronous or lag correlations between snow indices and solar parameters on multi-time scales. In particular, the Spring Snow Day Number (SSDN) is of significant synchronous or lag correlation with SRF, SSN, and TSI on multi-time scales. It is further found that SSDN over the Tibetan Plateau has more stable positive correlations with SRF by using the 21-year running mean and cross spectrum analyses. Therefore, SSDN can be ascertained to be the most sensitive snow index to the solar activity compared with other snow indices. Moreover, its influence on summer precipitation of China is strongly regulated by solar activity. In high solar activity years (HSAY), the significant correlated area of summer precipitation in China to SSDN is located further north than that in low solar activity years (LSAY). Such impact by solar activity is also remarkable after excluding the impact of ENSO (i.e., El Niño–Southern Oscillation) events. These results provide support for the application of snow indices in summer rainfall prediction in China.

Early warm-season mesoscale convective systems dominate soil moisture–precipitation feedback for summer rainfall in central United States

Land-atmosphere interactions play an important role in summer rainfall in the central United States, where mesoscale convective systems (MCSs) contribute to 30 to 70% of warm-season precipitation. Previous studies of soil moisture-precipitation feedbacks focused on the total precipitation, confounding the distinct roles of rainfall from different convective storm types. Here, we investigate the soil moisture-precipitation feedbacks associated with MCS and non-MCS rainfall and their surface hydrological footprints using a unique combination of these rainfall events in observations and land surface simulations with numerical tracers to quantify soil moisture sourced from MCS and non-MCS rainfall. We find that early warm-season (April to June) MCS rainfall, which is characterized by higher intensity and larger area per storm, produces coherent mesoscale spatial heterogeneity in soil moisture that is important for initiating summer (July) afternoon rainfall dominated by non-MCS events. On the other hand, soil moisture sourced from both early warm-season MCS and non-MCS rainfall contributes to lower-level atmospheric moistening favorable for upscale growth of MCSs at night. However, soil moisture sourced from MCS rainfall contributes to July MCS rainfall with a longer lead time because with higher intensity, MCS rainfall percolates into deeper soil that has a longer memory. Therefore, early warm-season MCS rainfall dominates soil moisture-precipitation feedback. This motivates future studies to examine the contribution of early warm-season MCS rainfall and associated soil moisture anomalies to predictability of summer rainfall in the major agricultural region of the central United States and other continental regions frequented by MCSs.

Dynamical and Machine Learning Hybrid Seasonal Prediction of Summer Rainfall in China

The seasonal prediction of summer rainfall is crucial for regional disasters reduction, but currently has a low prediction skill. We developed a dynamical and machine learning hybrid (MLD) seasonal prediction method for summer rainfall in China based on circulation fields from the CAS FGOALS-f2(无全称) operational dynamical prediction model. By selecting the optimum hyperparameters for three machine learning methods to obtain the best fit and least overfitting, an ensemble mean of the random forest and gradient boosting regression tree methodswas shown to have the highest prediction skill measured by the anomalous correlation coefficient. The skill had anaverage value of 0.34 in the historical cross-validation period (1981–2010) and 0.20 in a 10-yr period (2011–2020) of independent prediction, which significantly improves the dynamical prediction skill by 400%. Both reducing overfitting and using the best dynamical prediction are important in applications of the dynamical and MLD method and thisrequires further investigation.

Skillful prediction of summer rainfall in the Tibetan Plateau on multiyear time scales.

Skillful near-term climate predictions of rainfall over the Tibetan Plateau (TP), the Asian water tower, benefit billions of people. On the basis of the state-of-the-art decadal prediction models, we showed evidence that although the raw model outputs show low predicted ability for the summer Inner TP (ITP) rainfall due to low signal-to-noise ratios in models, we can produce realistic predictions by extracting the predictable signal from large ensemble predictions along with a postprocessing procedure of variance adjustment. The results indicate that the summer ITP rainfall is highly predictable on multiyear time scales. The predictability of ITP rainfall originates from the Silk Road pattern driven by sea surface temperature over the subpolar gyre region in North Atlantic. Real-time forecasts suggest that the ITP will become wetter, with 12.8% increase in rainfall during 2020-2027 relative to 1986-2005. Our results will help the water resources management in the surrounding regions.

Skillful prediction of summer rainfall in the roof of the world on multi-year timescales

Skillful prediction of summer rainfall in the roof of the world on multi-year timescales

Skillful prediction of summer rainfall in the roof of the world on multi-year timescales

Skillful prediction of summer rainfall in the roof of the world on multi-year timescales

The effectiveness of ARIMAX model for prediction of summer rainfall in northwest Western Australia

Inherent climate mechanism plays a large role in Australian rainfall events, while most interestingly, such influence of climate drivers on rainfall generation depends largely on a place’s geographical location and the season it has been passing through. For Northwest Western Australia (NWWA), summer has been the main rainfall season; therefore, it became necessary to develop a seasonal forecast model several months ahead of the event. This study involves the selection of potential climate drivers for NWWA and using them to forecast and model long-term seasonal summer rainfall (Dec to Feb) several months advance. Two rainfall stations (Bidyadanga and Gogo) were selected considering the availability of continuous monthly rainfall data (from 1915 to 2015) with a minimal missing value. Simple multiple linear regression (MLR) and ARIMAX have been performed on acquired rainfall data and lagged climate indices. It has been observed that the Southern Oscillation Index (SOI) and the Western Indian Ocean Index (WIO) has a great influence on NWWA rainfall. As such, the WIO-SOI model (4 months advance) shows a significantly higher correlation in the ARIMAX model (0.68 and 0.65 for Bidyadanga and Gogo station, respectively) compared to the MLR model (0.35 and 0.36) and confirms its effectiveness to be used for rainfall prediction in the region. It is believed that the simple but effective nature of the developed models can produce optimum benefit on stakeholder’s decision making to tackle future socio-economic challenges.

Subseasonal prediction and predictability of summer rainfall over eastern China in BCC_AGCM2.2

The present study examines subseasonal prediction skills and biases of the summer rainfall over eastern China in the Beijing Climate Center (BCC) Atmospheric General Circulation Model (BCC_AGCM2.2) and assesses the predictability of eastern China summer rainfall based on the multi-member forecasts. The BCC_AGCM2.2 model shows some skill in predicting summer rainfall over eastern China within the lead-times of 0–9 days. However, the subseasonal prediction skill is low on average, which is linked to the ability of the model in predicting the western Pacific subtropical high (WPSH). The low prediction skills may partially be attributed to biases in the model, including a wider meridional span and a weaker intensity of rainbelt in Yangtze River valley, earlier meridional movement, and a further northward shift of WPSH compared to that in the observations. These biases result in an obvious dry bias along the monsoon rainbelt, and a wet bias to both the north and south sides. Moreover, as a major contributor to the predictability, the Pacific-Japan pattern is not well predicted for both its spatial pattern and subseasonal evolution. The low forecast skill of summer rainfall in eastern China seems due to a dominant role of the atmospheric internal variability and a minor influence of the sea surface temperature in extratropical climate variability. Nevertheless, enhanced prediction skills under the assumption of a perfect model imply the potential to improve the prediction skill of the summer rainfall over eastern China through reducing model biases.

Inter-annual variability and skill of tropical rainfall and SST in APCC seasonal forecast models

The present study explored the performance of the current coupled models obtained from the Asia Pacific Economic Cooperation (APEC) Climate Centre (APCC) in representing the tropical Indo-Pacific sea surface temperature (SST) and rainfall during boreal summer season (June through September; JJAS). We have used the retrospective/hindcast runs for 28 years from 1983 to 2010 initialized in May. The mean SST bias in the tropical Indo-Pacific Oceans showed large diversity among the models in JJAS. In the case of the rainfall, most of the models displayed a strong dry bias over the major continental regions and wet biases over the tropical oceans. The majority of the models simulated the Inter-annual variability (IAV) of JJAS rainfall and SST reasonably well over the equatorial Pacific region, where the models are close to observed IAV and maximum signal to noise ratio (SNR). It is found that the models display, low IAV of rainfall and SST over the Indian Ocean with low SNR values, resulting in less predictive skill as compared to the tropical Pacific region. Similarly, all models showed a higher skill in summer rainfall prediction over the oceanic regions compared to the Asian land region, where SNR is very low. Further analysis suggested that the models have greater skill in predicting El Nino-Southern Oscillation (ENSO). The category wise analysis showed that models could predict 60–70% of the extreme ENSO events, but the normal events are represented only by 50%. It is noted that the models predict many false alarms for El Nino resulting in a higher frequency of El Nino occurrence. This is mainly responsible for stronger ENSO and the Asian Monsoon teleconnections in the models than in the observations. Meanwhile, the category wise rainfall skill for extended Indian monsoon region (EMR) displayed 50–60% accuracy for the extreme monsoon years and is around 50% for normal years. However, models such as CCSM3, CFSV2, and CANCM3 have displayed higher rainfall skills over EMR as compared to the other models possibly due to better representation of teleconnections spatial patterns between EMR rainfall and SST anomalies over Indo-Pacific Oceans.

Improving the prediction skill for China summer rainfall through correcting leading modes in Beijing Climate Center's Climate System Model

In this study, the performance of Beijing Climate Center's Climate System Model (BCC_CSM) is analysed in terms of China summer rainfall prediction, especially its capability to reproduce the spatial structure and temporal variation of the leading modes of empirical orthogonal function decomposition. Results show that the BCC_CSM has predictability for the summer mean rainfall pattern, namely, reproducing more rainfall in South China and maritime area, and less rainfall in North China and inland area. The model can reproduce the spatial pattern of the first two leading modes of observed summer rainfall; however, it fails to show correct interannual variability of the first two principle components, which may lead to a low skill in summer rainfall anomaly prediction over most parts of China. The leading mode‐based correction (LMC) method is then applied for the post processing procedure to correct prediction errors. Cross validation confirms that the LMC method can integrate the historical spatial pattern information of observed summer rainfall with the original model prediction, making the corrected model output better reflect spatial structure of the leading modes and their interannual variation. Compared with the original model output, the positive temporal correlation coefficient area for the corrected China summer rainfall anomalies is thus increased, and the average spatial correlation coefficient during 1991–2015 is improved from −0.01 before the correction to 0.17. The LMC method shows its potential for improving the prediction skill of BCC_CSM, which can be used in operational prediction.

Prediction of summer rainfall over the source region of the Blue Nile by using teleconnections based on sea surface temperatures

Summer rainfall (June–September) accounts for about 80% of the annual rainfall in the Lake Tana basin of Ethiopia, the source region of the Blue Nile River. Prediction of summer rainfall would be valuable for managing the region’s water resources and agricultural operations. This study investigated the influence of sea surface temperature (SST) as a predictor of summer rainfall in the basin by applying cross-correlation analysis between summer rainfall and climatic indices and SST in various oceanic regions from 1985 to 2015. Summer rainfall showed a strong negative correlation (− 0.619) with the Pacific Decadal Oscillation index at short time lags. Further analysis identified strong teleconnections (|r| ≥ 0.5) between SSTs in specific parts of the Pacific Ocean and summer rainfall in the Lake Tana basin, raising the possibility of predicting summer rainfall from Pacific SSTs with a lead time of 4 to 5 months. Average SSTs of an area near the Philippines and an area west of North America were positively correlated (0.629) and negatively correlated (− 0.538), respectively, with summer rainfall in the Lake Tana basin. Predictions of summer rainfall from these teleconnected SSTs by an Elman recurrent neural network model were encouraging, indicating a strong correlation (r > 0.77) between the observed and predicted summer rainfall. Our results suggest that neural network techniques may have sufficient skill to support seasonal rainfall predictions over the Lake Tana basin.

Seasonal Predictability of Summer Rainfall over South America

The seasonal predictability of austral summer rainfall is evaluated in a set of retrospective forecasts (hindcasts) performed as part of the Minerva and Metis projects. Both projects use the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) coupled to the Nucleus for European Modelling of the Ocean (NEMO). The Minerva runs consist of three sets of hindcasts where the spatial resolution of the model’s atmospheric component is progressively increased while keeping the spatial resolution of its oceanic component constant. In the Metis runs, the spatial resolution of both the atmospheric and oceanic components are progressively increased. We find that raw model predictions show seasonal forecast skill for rainfall over northern and southeastern South America. However, predictability is difficult to detect on a local basis, but it can be detected on a large-scale pattern basis. In addition, increasing horizontal resolution does not lead to improvements in the forecast skill of rainfall over South America. A predictable component analysis shows that only the first predictable component of austral summer precipitation has forecast skill, and the source of forecast skill is El Niño–Southern Oscillation. Seasonal prediction of precipitation remains a challenge for state-of-the-art climate models. Positive benefits of increasing model resolution might be more evident in other atmospheric fields (i.e., temperature or geopotential height) and/or temporal scales (i.e., subseasonal temporal scales).