Objective and probabilistic long-range forecasts of summertime air temperatures in South Korea based on Gaussian processes

We propose the objective long-range forecasting model based on Gaussian processes (OLRAF-GP), focusing on summertime near-surface air temperatures in June (1-month lead), July (2-month lead), and August (3-month lead). The predictors were objectively selected based on their relationships with the target variables, either from observations (GP-OBS) or from observations and dynamical climate model results from APEC Climate Center multi-model ensemble (APCC MME) for the period with no observed data (GP-MME). The performances of the OLRAF-GP models were compared with the model with pre-determined predictors from observations (GP-PD). Both GP-MME and GP-OBS outperformed GP-PD in June (Heidke skill score; HSS = 0.46, 0.72, and 0.16 for mean temperature) and July (HSS = 0.53, 0.3, and 0.07 for mean temperature). Furthermore, GP-MME mostly outperformed GP-OBS and GP-PD in August (HSS = 0.52, 0.28, and 0.5, respectively, for mean temperature), implying larger contributions of the additional predictors from MME. OLRAF-GP models, especially GP-MME, are expected to better forecast summertime temperatures in regions where existing models have been struggling. We find that the physical processes associated with the notable predictors are aligned with those in previous studies, such as the attribution of the La Niña conditions in the previous winter, the related Indian Ocean capacitor effect, and the impacts of wintertime Polar/Eurasia pattern. These results imply that the mechanisms of the objectively selected predictors can be physically meaningful, and their inclusion can improve model performance and efficiency.

QPF Model for lower Yamuna catchment, synoptic analogue method

The paper formulates a synoptic analogue model for issuing Quantitative Precipitation Forecast (QPF) for Lower Yamuna Catchment (LYC) based upon eleven years data (1998-2008) during southwest monsoon season. The results so derived were verified with realized Average Areal Precipitation (AAP) for the corresponding synoptic situation during 2009 southwest monsoon season. The performance of the model was observed Percentage Correct (PC) up to 86 % and for extreme events showed 100% correct with Heidke Skill Score (HSS) value 0.9. The experience during south west monsoon 2009 has shown that Synoptic analogue model can produce 24 hours advance QPF with accuracy and greater skill to facilitate the flood forecasters of Central Water Commission.

Verification of operational rainfall forecast over eastern India during southwest monsoon season

The Indian summer monsoon rainfall forecast and its verification has a direct impact on various sectors of public interest besides economy of the country. The present study highlights the verification of distribution forecast of synoptic method issued daily for six met subdivisions, comprising of five states of eastern India namely West Bengal, Sikkim, Bihar, Jharkhand and Odisha. Three years monsoon season rainfall data from 2011 through 2013 are used for the study area. The distribution-oriented verification is done for different rainfall classes like dry, isolated, scattered, fairly widespread and widespread to understand the usefulness of the synoptic method. Statistics are presented for both combined classes of Percentage Correct (PC) and Heidke Skill Score (HSS) of the met subdivision wise forecast and PC, POD and CSI for individual classes. It has been observed that among the met subdivision the efficiency of the method is highest in Sub Himalayan West Bengal (SHWB) & Sikkim followed by Gangetic West Bengal (GWB), Odisha, Jharkhand and Bihar.

Evaluation of heavy rainfall warning over India during summer monsoon season

India Meteorological Department (IMD) issues heavy rainfall warning for a meteorological sub-division when the expected 24 hours rainfall over any rain gauge station in that sub-division is likely to be 64.5 mm or more. Though these warnings have been provided since the inception of IMD, a few attempts have been made for quantitative evaluation of these warnings. Hence, a study is undertaken to verify the heavy rainfall warnings over 36 meteorological sub-divisions of India during monsoon months (June-September) and season as a whole. For this purpose, data of recent 5 years (2002-2006) has been taken into consideration. In this connection, the day when heavy rainfall is recorded over at least one station in a sub-division, has been considered as a heavy rainfall day for that sub-division. There is a large spatial and temporal variability in skill scores of heavy rainfall warnings over India during summer monsoon season. Considering the monsoon season as a whole, the Heidke Skill Score (HSS) is relatively less (<0.20) over the regions with less frequent heavy rainfall like Lakshadweep, southeast peninsula, Vidarbha, Marathwada, Jammu & Kashmir, Arunachal Pradesh and Nagaland, Manipur, Mizoram & Tripura (NMMT). It is higher (> 0.50) over Konkan & Goa, Madhya Maharashtra and Gujarat region. There has been improvement in the forecast skill with gradual increase in the critical success index and Heidke skill score over the years mainly due to the Numerical Weather Prediction (NWP) models' guidance available to the forecasters. However, the false alarm rate and missing rate are still very high (> 0.50), especially over many sub-divisions of northwest India, southeast peninsula and NMMT.

Large-Sample Application of Radar Reflectivity Object-Based Verification to Evaluate HRRR Warm-Season Forecasts

The Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR from April – September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern US during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE.HRRR tends to over-forecast all objects, but substantially over-forecasts both small objects at low reflectivity thresholds and large objects at high reflectivity thresholds. HRRR tends to either under-forecast objects in the southern and central Plains or has a correct frequency bias there, whereas it over-forecasts objects across the southern and eastern US. Attribute comparisons reveal the inability of the HRRR to fully resolve convective scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts.Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke Skill Score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.

Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River

In the last decades since the dramatic increase in flood frequency and magnitude, floods have become a crucial problem in West Africa. National and international authorities concentrate efforts on developing early warning systems (EWS) to deliver flood alerts and prevent loss of lives and damages. Usually, regional EWS are based on hydrological modeling, while local EWS adopt field observations. This study aims to integrate outputs from two regional hydrological models—Niger HYPE (NH) and World-Wide HYPE (WWH)—in a local EWS developed for the Sirba River. Sirba is the major tributary of Middle Niger River Basin and is supported by a local EWS since June 2019. Model evaluation indices were computed with 5-day forecasts demonstrating a better performance of NH (Nash–Sutcliffe efficiency NSE = 0.58) than WWH (NSE = 0.10) and the need of output optimization. The optimization conducted with a linear regression post-processing technique improves performance significantly to “very good” for NH (Heidke skill score HSS = 0.53) and “good” for WWH (HSS = 0.28). HYPE outputs allow to extend local EWS warning lead-time up to 10 days. Since the transfer informatic environment is not yet a mature operational system 10–20% of forecasts were unfortunately not produced in 2019, impacting operational availability.

Machine Learning Applications to Kronian Magnetospheric Reconnection Classification

&lt;p&gt;Signatures of magnetic reconnection in Saturn's magnetotail are identified in magnetometer observations by characteristic deviations in the northward component of the magnetic field. These magnetic deflections are caused by travelling plasma structures created by reconnection rapidly passing over the observing spacecraft. The identification of these reconnection signatures has long been performed by eye, and more recently through semi-automated methods, however these methods are often limited through a required human verification step. Here, we present a fully automated, supervised learning machine learning (ML) model to identify evidence of reconnection in Cassini MAG (magnetometer) observations of the Kronian magnetosphere, constructed using the Smith et al, 2016. reconnection catalogue which contains hundreds of examples of plasmoids, travelling compression regions and dipolarizations. This ML model is capable of rapidly identifying reconnection events in large time-span Cassini datasets, tested against the full year of 2010 with a high level of accuracy (99\%), true skill score (0.97), and Heidke skill score (0.85). From this ML model, a full cataloguing and examination of magnetic reconnection in the Kronian magnetosphere across Cassini's near Saturn lifetime is now possible.&lt;/p&gt;

Comparison of TRMM Microwave Imager Rainfall Datasets from NASA and JAXA

This study compares three TMI rainfall datasets generated by two versions of NASA’s Goddard Profiling algorithm (GPROF2010 and GPROF2017) and JAXA’s Global Satellite Mapping of Precipitation algorithm (GSMaP) over land, coast, and ocean. We use TRMM precipitation radar observations as the reference, and also include CloudSat cloud profiling radar (CPR) observations as the reference over ocean. First, the dynamic thresholds for rainfall detection used by GSMaP and GPROF2017 have better detection capability, indicating by larger Heidke skill score (HSS) values, compared with GPROF2010 over both land and coast. Over ocean, all three datasets have very similar HSS regardless of including CPR observations. Next, intensity analysis shows that no single dataset performs the best according to all three statistical metrics (correlation, root-mean-square error, and relative bias), except that GSMaP performs the best for stratiform precipitation over coast, and GPROF2017 performs the best for convective precipitation over ocean, based on all three metrics. Finally, an error decomposition analysis shows that the total error and its three components have very different characteristics over several regions among these three datasets. For example, the positive total error in GPROF2010 and GSMaP is primarily caused by the positive hit bias over central Africa, while the false bias in GPROF2017 is largely responsible for this positive total error. For future algorithm development, results from this study imply that a convective–stratiform separation technique may be necessary to reduce the large underestimation for convective rain intensity.

Skills of Thunderstorm Prediction by Convective Indices over a Metropolitan Area: Comparison of Microwave and Radiosonde Data

In this work, we compare the values of 15 convective indices obtained from radiosonde and microwave temperature and water vapor profiles simultaneously measured over Nizhny Novgorod (56.2°N, 44°E) during 5 convective seasons of 2014–2018. A good or moderate correlation (with coefficients of ~0.7–0.85) is found for most indices. We assess the thunderstorm prediction skills with a lead time of 12 h for each radiosonde and microwave index. It is revealed that the effectiveness of thunderstorm prediction by microwave indices is much better than by radiosonde ones. Moreover, a good correlation between radiosonde and microwave values of a certain index does not necessarily correspond to similar prediction skills. Eight indices (Showalter Index, Maximum Unstable Convective Available Potential Energy (CAPE), Total Totals index, TQ index, Jefferson Index, S index, K index, and Thompson index) are regarded to be the best predictors from both the true skill statistics (TSS) maximum and Heidke skill score (HSS) maximum points of view. In the case of radiosonde data, the best indices are the Jefferson Index, K index, S index, and Thompson index. Only TSS and HSS maxima for these indices are close to the microwave ones, whereas the prediction skills of other radiosonde indices are essentially worse than in the case of microwave data. The analysis suggests that the main possible reason of this discrepancy is an unexpectedly low quality of radiosonde data.

Validating the Space Weather Modeling Framework (SWMF) for applications in northern Europe

In this study we investigate the performance of the University of Michigan’s Space Weather Modeling Framework (SWMF) in prediction of ground magnetic perturbations (ΔB) and their rate of change with time (dB/dt), which is directly connected to geomagnetically induced currents (GICs). We use the SWMF set-up where the global magnetosphere provided by the Block Adaptive Tree Solar-wind Roe-type Upwind Scheme (BATS-R-US) MHD code, is coupled to the inner magnetosphere and the ionospheric electrodynamics. The validation is done for ΔB and dB/dt separately. The performance is evaluated via data-model comparison through a metrics-based approach. For ΔB, the normalized root mean square error (nRMS) and the correlation coefficient are used. For dB/dt, the probability of detection, the probability of false detection, the Heidke skill score, and the frequency bias are used for different dB/dt thresholds. The performance is evaluated for eleven ground magnetometer stations located between 59° and 85° magnetic latitude and spanning about five magnetic local times. Eight geomagnetic storms are studied. Our results show that the SWMF predicts the northward component of the perturbations better at lower latitudes (59°–67°) than at higher latitudes (>67°), whereas for the eastward component, the model performs better at high latitudes. Generally, the SWMF performs well in the prediction of dB/dt for a 0.3 nT/s threshold, with a high probability of detection ≈0.8, low probability of false detection (<0.4), and Heidke skill score above zero. To a large extent the model tends to predict events as often as they are actually occurring in nature (frequency bias 1). With respect to the metrics measures, the dB/dt prediction performance generally decreases as the threshold is raised, except for the probability of false detection, which improves.