Anomaly Correlation Research Articles

Seasonal Forecasting of the Onset of the Rainy Season in West Africa

Seasonal forecasts for monsoonal rainfall characteristics like the onset of the rainy seasons (ORS) are crucial for national weather services in semi-arid regions to better support decision-making in rain-fed agriculture. In this study an approach for seasonal forecasting of the ORS is proposed using precipitation information from a global seasonal ensemble prediction system. It consists of a quantile–quantile-transformation for eliminating systematic differences between ensemble forecasts and observations, a fuzzy-rule based method for estimating the ORS date and graphical methods for an improved visualization of probabilistic ORS forecasts. The performance of the approach is tested for several climate zones (the Sahel, Sudan and Guinean zone) in West Africa for a period of eleven years (2000 to 2010), using hindcasts from the Seasonal Forecasting System 4 of ECMWF. We indicated that seasonal ORS forecasts can be skillful for individual years and specific regions (e.g., the Guinean coasts), but also associated with large uncertainties. A spatial verification of the ORS fields emphasizes the importance of selecting appropriate performance measures (e.g., the anomaly correlation coefficient) to avoid an overestimation of the forecast skill. The graphical methods consist of several common formats used in seasonal forecasting and a new index-based method for a quicker interpretation of probabilistic ORS forecast. The new index can also be applied to other seasonal forecast variables, providing an important alternative to the common forecast formats used in seasonal forecasting. Moreover, the forecasting approach proposed in this study is not computationally intensive and is therefore operational applicable for forecasting centers in tropical and subtropical regions where computing power and bandwidth are often limited.

Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe

AbstractA statistical learning approach to produce seasonal temperature forecasts in western Europe and Scandinavia was implemented and tested. The leading principal components (PCs) of sea surface temperature (SST) and the geopotential at the 150-hPa level (GPT) were derived from reanalysis datasets and used at different lags (from one to five seasons) as predictors. Random sampling of both the fitting years and the potential predictors together with the Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to create a large ensemble of statistical models. Applying the models to independent test years shows that the ensemble performs well over the target areas and that the ensemble mean is more accurate than the best individual ensemble member on average. Skillful results were especially found for summer and fall, with the anomaly correlation coefficient values ranging between 0.41 and 0.68 for these seasons. The correct simulation of decadal trends, using sufficiently long time series for fitting (70 years), and the use of lagged predictors increased the prediction skill. The decadal-scale variability of SST, most importantly the Atlantic multidecadal oscillation (AMO), and different PCs of GPT are the most important individual predictors among all predictors. Both SST and GPT bring equally much predictive power, although their importance is different in different seasons.

Extended-Range Runoff Forecasting Using a One-Way Coupled Climate–Hydrological Model: Case Studies of the Yiluo and Beijiang Rivers in China

Extended-range runoff forecasting is important for water resources management and energy planning. Experimental extended-range runoff was hindcasted, based on an extended-range climate model, developed by National Climate Center of the China Meteorological Administration, and semi-distributed hydrological model HBV-D. The skill of the runoff forecasts was explored using mean square skill score (MSSS), anomaly correlation coefficient (ACC), and areas under the relative operating characteristics curve (AUC) for three terciles for three experimental 51-day periods during flood season (June 1 to July 21, July 1 to August 20 and August 1 to September 20) for two rivers in China. The results revealed decreasing trends of the five indices, and varying length of the continuous longest skilful time slice from 3 days to 6 weeks depending on index, period and river location. In most cases, skilful abnormal terciles forecast occurred more often or with similar frequency to deterministic forecasts. It suggests that ensemble probability forecasting is a method with potential for extended-range river runoff forecast. Further, abnormal terciles are more skillful than normal terciles, and above normal are more skillful than below normal. In terms of a temporal mean of the MSSS and ACC, deterministic forecasts are skillful for both rivers in all three periods, but more skillful for the Beijiang River than for the Yiluo River in most cases.

Improving precipitation forecasts over Iran using a weighted average ensemble technique

In this paper, the ensemble-weighted mean (ENSWM) technique is experimented for improving 24- to 72-hr precipitation forecasts over Iran during autumn and winter 2011 and 2012. The ensemble prediction system (EPS), used in this research, consists of nine different configurations of the weather research and forecasting model. In this technique, weights for each ensemble member at each grid point are assigned on the basis of the correlation coefficient (CC) between ensemble members and observed daily rainfall during a training period. Apart from ENSWM, precipitation forecasts using the simple ensemble mean (ENSM) are also generated and compared. Results showed that, in general, the forecast errors are relatively high along the coasts of the Caspian Sea in northern and at the Zagros mountainous areas located in western Iran. The skill of the rainfall forecasts of the ENSWM is examined against ENSM and individual members of the ensemble. The 24- to 72-hr forecasts are evaluated using common statistical scores including root mean-squared error (RMSE), and anomaly CC (ACC) for continuous forecasts and probability of detection (POD) score and threat score for categorical forecasts. The comparison reveals that the ENSWM is able to provide more accurate forecast of rainfall over Iran by taking the strength of each constituent member of the ensemble. It has been further found that the precipitation forecast skill of ENSWM is higher than ENSM and each ensemble member in the short-range time scale over Iran. The rainfall prediction skill over Iran was improved significantly using the weighted ENSWM technique. Results clearly show the advantage of using an EPS for the prediction of precipitation over the country vs. a single deterministic forecast for operational purposes. The RMSE of 24-, 48- and 72-hr forecasts in ENSWM relative to ENSM is reduced by 2, 2 and 5%, respectively. The CC increased by 15% in the ENSWM relative to ENSM.

Potential predictability of Arabian peninsula summer surface air temperature in the North American multimodel ensemble

The present study evaluates the potential predictability, and prediction skill of surface air temperature (SAT) over the Arabian peninsula (AP) referred to as AP-SAT hereafter, during boreal summer (June–July–August: JJA) from 1982 through 2017. The study was made by considering the single model, and a multimodel ensemble (MME) approach. The seasonal prediction data for JJA SAT initialized at May (Lead-1), and April (Lead-2) observed initial conditions, from six coupled atmosphere–ocean global circulation models included in the North American Multimodel Ensemble, is utilized. The potential predictability (PP) is estimated through the estimation of the signal-to-noise ratio (S/N ratio) and perfect model correlation (PMC), while prediction skill is computed by the temporal anomaly correlation coefficient (TCC). All models show a decrease in potential predictability and prediction skill with an increase in lead time. The CFSv2 and NASA models show higher PP, which indicates a high potential predictive skill for summer AP-SAT in these models. However, both models show quite low values of TCC all over the AP domain, which is an indication of overconfident predictions. The three geophysical fluid dynamics laboratory models show high prediction skill at both leads. An essential finding of the predictive analysis (PMC and TCC) is that the MME does outperform the individual model at both leads for summer AP-SAT. Each model captures the observed relationship between spatially averaged AP-SAT with sea surface temperature (SST) and 200 hPa geopotential height (Z200) during JJA, with varying details. Persistent model biases impact negatively model predictability and skill, and better AP-SAT and SST teleconnection pattern in models lead to higher predictability. Improvements in initial conditions, model physics, and larger ensemble size are necessary elements to enhance the summer AP-SAT potential predictability and skill.

Verification of Two Years of CNR-ISAC Subseasonal Forecasts

Abstract The monthly forecasting system of the Institute of Atmospheric Sciences and Climate of the National Research Council (CNR-ISAC) of Italy is operationally run on a weekly basis in the framework of the Subseasonal-to-Seasonal (S2S) project to produce 41-member ensemble forecasts. The first two years of forecasts, covering 106 weeks from April 2015, are verified against ERA-Interim as weekly averages starting from the first forecast day. Nonprobabilistic scores of 500-hPa geopotential height and 850-hPa temperature anomalies are computed for the extratropical hemispheres. The anomaly correlation coefficient shows enhanced predictive skill during the cold months, when favorable values are occasionally obtained beyond week 2. The root-mean-square error saturates toward the climatological value between weeks 2 and 3. Reliability diagrams are used to evaluate the probabilistic forecast skill of 2-m temperature over Northern Hemisphere extratropical land points, in terms of above- and below-normal events. The forecasting system loses reliability and resolution beyond week 2, but well reproduces the observed 2-yr mean frequency up to week 4, proving to be unbiased. The reliability of the forecasting system systematically outperforms that obtained by persisting the previous week forecast. Beyond week 2, the forecast distribution of below-normal events shows low confidence. However, a reliability diagram based on equally populated bins of forecast probabilities highlights residual resolution up to week 4 at low probabilities. ROC diagrams confirm that the modeling system has greater discrimination capability for below-normal events. The reliability analysis of accumulated precipitation shows minor differences between below- and above-normal events, with lower skill than 2-m temperature.

Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models

Recent studies have shown that the Madden-Julian Oscillation (MJO) is significantly modulated by the stratospheric Quasi-Biennial Oscillation (QBO). In general, boreal winter MJO becomes more active during the easterly phase of the QBO (EQBO) than during the westerly phase (WQBO). Based on this finding, here we examine the possible impacts of the QBO on MJO prediction skill in the operational models that participated in the WCRP/WWRP subseasonal-to-seasonal (S2S) prediction project. All models show a higher MJO prediction skill during EQBO winters than during WQBO winters. For the bivariate anomaly correlation coefficient of 0.5, the MJO prediction skill during EQBO winters is enhanced by up to 10 days. This enhancement is insensitive to the initial MJO amplitude, indicating that the improved MJO prediction skill is not simply the result of a stronger MJO. Instead, a longer persistence of the MJO during EQBO winters likely induces a higher prediction skill by having a higher prediction limit.

Assessing Seasonal Predictability Sources and Windows of High Predictability in the Climate Forecast System, Version 2

The representation of ENSO and NAO are examined in the Climate Forecast System, version 2 (CFSv2), reforecasts with a focus on the physical processes related to teleconnections and predictability. CFSv2 predicts ENSO well, but an eastward shift of the tropical Pacific sea surface temperature (SST) anomalies is evident. Although it appears minor on the global scale, the shift in convection and the large-scale wave train affects the model prediction of regional climate. In contrast, NAO is predicted poorly. The anomaly correlation coefficient (ACC) between the model ensemble mean and the observation is 0.27 during 1982–2010, and the ensemble spread is large. The representation of three sources of NAO predictability—SST, the stratospheric polar vortex, and the Arctic sea ice concentration—is investigated. It is found that the link between tropical Pacific SST and NAO is not well represented in CFSv2, and that the tropospheric–stratospheric interactions are too weak, both contributing to the poor prediction of NAO. Additionally, the impact of ENSO and NAO on prediction skill of CFSv2 in boreal winter is analyzed in terms of the spatial ACC of geopotential height. Active ENSO events exhibit larger prediction skill than neutral years, especially during the ENSO+/NAO− and ENSO−/NAO+ winters. Spatial patterns of prediction skill are also examined, and larger skill of geopotential height and 2-m air temperature is found outlined by the nodes of the PNA pattern, consistent with the large signal-to-noise ratios associated with the ENSO teleconnection.

Predictive performance of NMME seasonal forecasts of global precipitation: A spatial-temporal perspective

Global climate models (GCMs) produce informative seasonal forecasts of global precipitation months ahead of the occurrence for hydrological forecasting. Meanwhile, the skill of GCM forecasts varies by location and initialization time. In this paper, we investigate the anomaly correlation, which indicates the correspondence between forecasts and observations, for 10 sets of global precipitation forecasts in the North American Multi-Model Ensemble (NMME) project. We propose to use principal component analysis to characterize the variation of anomaly correlation. We identify the existence of spatial and temporal patterns at the global scale. The spatial pattern reveals that high (low) anomaly correlation at one initialization time coincides with high (low) anomaly correlation at other initialization times. In other words, for a grid cell, the anomaly correlation at different initialization times tends to be similarly high, or low. It is observed that some of the regions where grid cells are with overall high anomaly correlation tend to exhibit tele-connections with global climate drivers. On the other hand, the temporal pattern suggests that the anomaly correlation tends to improve with initialization time. This pattern is attributable to data assimilation that bases forecasts at a later initialization time on more global observations and simulations. Generally, the two patterns are effective and explain 50% to 70% of the variation of anomaly correlation for the 10 sets of NMME forecasts. The projections of anomaly correlation vectors onto the two patterns help illustrate where and when the NMME precipitation forecasts are skillful.

Extended Spectral Analysis of Tidal Variability in the North Atlantic Ocean

AbstractOcean tides have experienced large‐scale changes over the past century, in concert with regionally variable global mean sea level (MSL) rise. Additionally, there can be coherencies between MSL and tidal fluctuations that are active at shorter time scales. This combination of water level variabilities may enhance the probabilities of exceeding flood levels under high‐tide events, leading to increased frequency of short‐term coastal inundation and nuisance flooding. Previous studies have established the tidal anomaly correlation (TAC) method to analyze covariability of individual tidal components and MSL in the Pacific Ocean, as well as their combination as a proxy for the change in the highest astronomical tide (δ‐HAT). Here we extend this methodology to new regions and perform analyses of 170 tide gauges in the North Atlantic Ocean, considering the eight largest gravitational tides and seven overtides. Results indicate that nearly all gauges (95%) exhibit strong individual TACs in one or more tidal constituents, and over half show significant δ‐HATs, with a near‐equal occurrence of positive and negative tendencies. The most coherent connections of tides and MSL were found on the U.S. East Coast and in the marginal waters of Europe. At some locations, all tidal variabilities act in the same direction, which may amplify possible flood level. At other locations major parts of the tidal spectrum may counteract the MSL rise and partially mitigate extreme water levels due to tidal evolution. In either case, an understanding of regional tidal changes correlated to MSL changes can be instructive in guiding future coastal development efforts.

Statistical Downscaling Precipitation Forecast for Hydropower Industry and Its Calibration Using Frequency Matching Method

This study deals with high-resolution precipitation forecasts for hydropower industry using a statistical downscaling method based on the linear regression of the categorized daily precipitation forecasts taken from the European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), the US National Centers for Environmental Prediction (NCEP), and the United Kingdom Met Office (UKMO), in the TIGGE archive as well as the quality-controlled precipitation data from China Merged Precipitation Analysis (CMPA). To further improve the forecast skill of the daily precipitation, the calibration of the precipitation forecast has been performed by using a statistical postprocessing approach called the frequency matching method (FMM). The results show that the statistical downscaling forecast skill using categorized precipitation scheme is much larger than that of bilinear interpolation and that using uncategorized precipitation scheme in terms of the equitable threat score (ETS), anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), no matter the precipitation is light rain, moderate rain, or heavy rain and the above. The calibration of the precipitation forecasts using FMM can significantly reduce the false alarm of the light rain and the missing rate of the heavy rain and the above. Hence, it can improve the inflow forecast skill in the hydrological models which make use of observed and predicted precipitation as input variables.

Factors Limiting the Forecast Skill of the Boreal Summer Intraseasonal Oscillation in a Subseasonal-to-Seasonal Model

In this study, we evaluate the forecast skill of the subseasonal-to-seasonal (S2S) prediction model of the Beijing Climate Center (BCC) for the boreal summer intraseasonal oscillation (BSISO). We also discuss the key factors that inhibit the BSISO forecast skill in this model. Based on the bivariate anomaly correlation coefficient (ACC) of the BSISO index, defined by the first two EOF modes of outgoing longwave radiation and 850-hPa zonal wind anomalies over the Asian monsoon region, we found that the hindcast skill degraded as the lead time increased. The ACC dropped to below 0.5 for lead times of 11 days and longer when the predicted BSISO showed weakened strength and insignificant northward propagation. To identify what causes the weakened forecast skill of BSISO at the forecast lead time of 11 days, we diagnosed the main mechanisms responsible for the BSISO northward propagation. The same analysis was also carried out using the observations and the outputs of the four-day forecast lead that successfully predicted the observed northward-propagating BSISO. We found that the lack of northward propagation at the 11-day forecast lead was due to insufficient increases in low-level cyclonic vorticity, moistening and warm temperature anomalies to the north of the convection, which were induced by the interaction between background mean flows and BSISO-related anomalous fields. The BCC S2S model can predict the background monsoon circulations, such as the low-level southerly and the northerly and easterly vertical shears, but has limited capability in forecasting the distributions of circulation and moisture anomalies.

Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China

Abstract. Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from the North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below −1 indicate drought events. The skill of the forecasting systems was evaluated in terms of anomaly correlation (AC) and Brier score (BS) or Brier skill score (BSS). The predictability for meteorological drought was quantified using AC and BS with a “perfect model” assumption, referring to the upper limit of forecast skill. The hydrological predictability was to distinguish the role of initial hydrological conditions (ICs) and meteorological forcings, which was quantified by root-mean-square error (RMSE) within the ESP (Ensemble Streamflow Prediction) and reverse ESP framework. The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanisms. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of ICs during cold and dry seasons. During wet seasons, there are no skillful hydrological predictions from lead month 2 onwards because of the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions was the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological drought predictability during the wet seasons in the MHRB.

Predicting the seasonal evolution of southern African summer precipitation in the DePreSys3 prediction system

We assess the ability of the DePreSys3 prediction system to predict austral summer precipitation (DJF) over southern Africa, defined as the African continent south of 15°S. DePresys3 is a high resolution prediction system (at a horizontal resolution of ~ 60 km in the atmosphere in mid-latitudes and of the quarter degree in the Ocean) and spans the long period 1959–2016. We find skill in predicting interannual precipitation variability, relative to a long-term trend; the anomaly correlation skill score over southern Africa is greater than 0.45 for the first summer (i.e. lead month 2–4), and 0.37 over Mozambique, Zimbabwe and Zambia for the second summer (i.e. lead month 14–16). The skill is related to the successful prediction of the El-Nino Southern Oscillation (ENSO), and the successful simulation of ENSO teleconnections to southern Africa. However, overall skill is sensitive to the inclusion of strong La-Nina events and also appears to change with forecast epoch. For example, the skill in predicting precipitation over Mozambique is significantly larger for the first summer in the 1990–2016 period, compared to the 1959–1985 period. The difference in skill in predicting interannual precipitation variability over southern Africa in different epochs is consistent with a change in the strength of the observed teleconnections of ENSO. After 1990, and consistent with the increased skill, the observed impact of ENSO appears to strengthen over west Mozambique, in association with changes in ENSO related atmospheric convergence anomalies. However, these apparent changes in teleconnections are not captured by the ensemble-mean predictions using DePreSys3. The changes in the ENSO teleconnection are consistent with a warming over the Indian Ocean and modulation of ENSO properties between the different epochs, but may also be associated with unpredictable atmospheric variability.

A newly developed APCC SCoPS and its prediction of East Asia seasonal climate variability

The Asia Pacific Economic Cooperation (APEC) Climate Center (APCC) in-house model (Seamless Coupled Prediction System: SCoPS) has been newly developed for operational seasonal forecasting. SCoPS has generated ensemble retrospective forecasts for the period 1982–2013 and real-time forecasts for the period 2014–current. In this study, the seasonal prediction skill of the SCoPS hindcast ensemble was validated compared to those of the previous operation model (APEC Climate Center Community Climate System Model version 3: APCC CCSM3). This study validated the spatial and temporal prediction skills of hindcast climatology, large-scale features, and the seasonal climate variability from both systems. A special focus was the fidelity of the systems to reproduce and forecast phenomena that are closely related to the East Asian monsoon system. Overall, both CCSM3 and SCoPS exhibit realistic representations of the basic climate, although systematic biases are found for surface temperature and precipitation. The averaged temporal anomaly correlation coefficient for sea surface temperature, 2-m temperature, and precipitation from SCoPS is higher than those from CCSM3. Notably, SCoPS well captures the northward migrated rainband related to the East Asian summer monsoon. The SCoPS simulation also shows useful skill in predicting the wintertime Arctic Oscillation. Consequently, SCoPS is more skillful than CCSM3 in predicting seasonal climate variability, including the ENSO and the Arctic Oscillation. Further, it is clear that the seasonal climate forecast with SCoPS will be useful for simulating the East Asian monsoon system.

Week 3\u20134 predictability over the United States assessed from two operational ensemble prediction systems

The subseasonal predictability of surface temperature and precipitation is examined using two global ensemble prediction systems (ECMWF VarEPS and NCEP CFSv2), with an emphasis on the week 3–4 lead (i.e. 15–28 days ahead) fortnight-average anomaly correlation skill over the United States, in each calendar season. Although the ECMWF system exhibits slightly higher skill for both temperature and precipitation in general, these two systems show similar geographical variations in the week 3–4 skill in all seasons and encouraging skill in certain regions. The regions of skill are then interpreted in terms of large-scale teleconnection patterns. Over the southwest US in summer, the North American monsoon system leads to higher skill in precipitation and surface temperature, while high skill over northern California in spring is found to be associated with the seasonal variability of the Arctic Oscillation (AO). During winter, in particular, week 3–4 predictability is found to be higher during extreme phases of the El Niño–Southern Oscillation, Pacific-North American (PNA)/Tropical-Northern Hemisphere mode, and AO/North Atlantic Oscillation (NAO). Both forecast systems are found to predict these teleconnection indices quite skillfully, with the anomaly correlation of the wintertime NAO and PNA exceeding 0.5 for both models. In both models, the subseasonal contribution to the PNA skill is found to be larger than for the NAO, where the seasonal component is large.

Multiweek Prediction Skill Assessment of Arctic Sea Ice Variability in the CFSv2

AbstractSkillful Arctic Sea ice prediction is becoming increasingly important because of its societal, industrial, and economic impacts over the polar regions and potential influence on lower-latitude weather and climate variability. In this work, we evaluate the multiweek forecast skill of Arctic sea ice using the Climate Forecast System, version 2 (CFSv2). To the authors’ knowledge, this is the first effort to diagnose and assess the skill of multiweek Arctic sea ice prediction from a coupled atmosphere–ocean model. Analysis of a suite of retrospective 45-day forecasts spanning 1999–2015 shows that CFSv2 captures general features of sea ice concentration (SIC) variability. Total SIC variability is dominated by interannual variability, which accounts for more than 60% of the total variance. Submonthly variability accounts for 29% of the total variance in December, 20% in March and June, and 12.5% in September. We assess the ability of CFSv2 to predict the pan-Arctic SIC, as well as regional SIC in nine Arctic regions. Results show that the SIC prediction skill is highly region dependent (e.g., higher prediction skill for Kara/Barents Seas and lower for the Canadian Archipelago). Overall, the maximum anomaly correlation coefficient (ACC) of SIC for both melt and freeze-up seasons is near the marginal zones, and their spatial distribution shows a relationship with the distribution of the variance. If the ACC of 0.5 is taken as the critical value for skillful prediction, the predictability of weekly SIC near the marginal zones is about 5–6 weeks. Prediction skill for Arctic sea ice extent is above 0.6 for the entire six target weeks and has a large contribution from interannual variability.

The Added Value of Assimilating Remotely Sensed Soil Moisture for Estimating Summertime Soil Moisture‐Air Temperature Coupling Strength

AbstractTo date, the direct use of remote‐sensing soil moisture data sets for examining surface/atmosphere coupling strengths has been hampered by the presence of significant random errors and data gaps in these products. This study investigates the potential for obtaining an improved observation‐based lower bound of summertime soil moisture‐air temperature coupling strength via the assimilation of long‐term, remote‐sensing soil moisture data sets into a simple, prognostic model driven by observed rainfall. In particular, we utilize simultaneous scatterometer‐ and radiometer‐based soil moisture products obtained from the European Space Agency Climate Change Initiative soil moisture product and a triple collocation analysis approach to estimate the variance of modeling and observation errors (required as input by a data assimilation system). Results show that assimilating remotely sensed soil moisture leads to larger coupling strength estimates as measured by the absolute anomaly correlation with independent temperature observation data than those obtained from modeled or remotely sensed soil moisture alone. According to an analyses of soil moisture error impacts on coupling strength estimates, this increase in coupling strength is likely attributable to an improvement in the precision of the soil moisture product used to estimate it. Based on this, we conclude that data assimilation provides an improved lower bound on the magnitude of true coupling strength between soil moisture and screen‐level air temperature.

The Impact of Vertical Resolution in the Assimilation of GPS Radio Occultation Data

Abstract The sensitivity of GPS radio occultation (GPSRO) bending angle assimilation to vertical resolution was studied within a global three-dimensional variational data assimilation (3DVAR) system. The sensitivity experiments were performed using different vertical resolutions of GPSRO data at 2 km, 1 km, 500 m, and 200 m. The assimilation of the higher vertical resolution GPSRO data showed better consistency in the analysis–forecast cycle in terms of the differences between GPSRO bending angle data and 6-h forecasts (O-F). This resulted in an improved analysis of the temperature, geopotential height, and wind in the mid-/upper-level troposphere by the hydrostatic response and the related model dynamics. It should be noted that the highest vertical resolution of the GPSRO data (200 m in this study) improved the forecasting skill level in terms of the root-mean-square error (against the European Centre for Medium-Range Weather Forecasts analysis) and the anomaly correlation of the geopotential height forecasting at 500 and 200 hPa in both the Northern and Southern Hemispheres. The benefits of assimilating higher vertical resolution GPSRO data were more pronounced in the upper-level troposphere, which was in agreement with previous studies using real GPSRO observations.

Prediction and predictability of Northern Hemisphere persistent maxima of 500-hPa geopotential height eddies in the GEFS

This study analyzes the predictability of the persistent maxima of 500-hPa geopotential height (Z500; PMZ) zonal eddies over the Northern Hemisphere in the long-term forecast datasets of the Global Ensemble Forecast System (GEFS) version 10. PMZ patterns, which potentially extending the predictability of severe weather events, include not only closed blocking anticyclones that occur more frequently in the Euro-Atlantic-Asia sector (EAAS) but also persistent open ridges and omega-shape blockings that prevail more often over the Pacific-North America sector (PNAS). The predicted PMZ occurrence frequencies in both the EAAS and the PNAS generally decrease with the lead time, which is consistent with classical blockings in early studies but different from the nearly invariant frequencies of blockings in a recent relevant diagnosis by Hamill and Kiladis. The Brier skill score associated with PMZ frequencies is generally higher in the PNAS than in the EAAS, indicating better predictions in the former. The forecast reliability decreases with the lead time in both sectors, particularly at the tails of probability distributions, suggesting some limitations of the GEFS. PMZ events longer than 1 week with anomaly correlation coefficients (ACCs) exceeding 0.6 in the Northern Hemisphere have a mean useful skill of nearly 10 lead days, which is approximately 0.5–1 day more than the average skill of all cases. Among these events, 50% extend useful ACC skills up to 12 days, and 25% extend the useful skill even further. A discussion is provided regarding how the better PMZ prediction skill in the PNAS can help improve 2 to 3-week predictions over North America.