Observed Precipitation Patterns Research Articles

Numerical simulations of the heavy rain event in the Democratic People's Republic of Korea during 9–10 August 2020

Accurate forecasting of heavy rainfalls and understanding of their dynamics are important to minimize the damage caused by them in the Democratic People's Republic of Korea (DPR Korea). This study is conducted on a heavy rainfall event (452 mm) on 9–10 August 2020 over Pankyo region located on the midlands of the Korean peninsula. To verify the proper configuration of convection-permitting simulations, sensitivity experiments were performed with five microphysical schemes (Lin, Goddard, Thompson, Morrison and WDM6) of the Weather Research and Forecasting (WRF) model. The results suggested that all high-resolution simulations reflect the main characteristics of observed precipitation pattern well, but the location and intensity of maximum precipitation from scheme to scheme. Among the considered all the microphysics, the Lin scheme showed the best agreement with observed precipitation. Results also showed that the Lin scheme reproduced the vertical distribution and time variation of several hydrometeors, as well as dynamic and thermodynamic parameters associated with heavy rainfall well. These outcomes suggest that the suitable selection of microphysics schemes with WRF model is important to predict and understand heavy rainfall events over the DPR Korea.

The influence of Urmia Lake desiccation on an extreme snowfall event: A case study using the WRF-Lake model

Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

Capability of satellite data to estimate observed precipitation in southeastern South America

AbstractPrecipitation is a fundamental component of the water cycle. Satellite‐derived precipitation estimates with high spatial resolution and daily to subdaily temporal resolution become very important in regions with a limited ground‐based measurement network, such as southeastern South America (SESA). This study evaluates the performance of four state‐of‐the‐art satellite products, including IMERG V.06 Final Run, PERSIANN, PERSIANN CCS‐CDR and PDIR‐NOW in representing observed precipitation over SESA during the 2001–2020 period. The ability of each product to represent observed annual and seasonal precipitation patterns was assessed. Statistical and categorical evaluation metrics were used to evaluate the performance of satellite precipitation estimates at monthly and daily timescales. Our results report that IMERG and CCS‐CDR achieve the best performance in estimating observed precipitation patterns at annual and seasonal timescales. While all satellite products effectively identify autumn and spring precipitation patterns, they struggle to represent winter and summer patterns. Notably, all satellite precipitation products have a better agreement with observed precipitation in wetter regions compared to drier regions, as indicated by the spatial distribution of continuous validation metrics. IMERG stands out as the most accurate product, reaching the highest correlation coefficients (0.75 < CC < 0.95) and Kling–Gupta efficiencies (0.65 < KGE < 0.85, rate as good to very good performance). Regarding categorical statistical metrics, IMERG correctly estimates the fraction of observed rainy days (POD > 0.7, CSI > 0.6) and shows the lowest fraction of estimated precipitation events that did not occur. PERSIANN, CCS‐CDR and PDIR‐NOW exhibit lower performances, mainly in drier areas. Moreover, PERSIANN and PDIR‐NOW tend to overestimate observed precipitation in almost the entire SESA region. We expect this validation study will provide greater reliability to satellite precipitation estimates, in order to provide an alternative that complement the scarce observed information available for decision‐making in water management and agricultural planning.

Cloud climatology of northwestern Mexico based on MODIS data

ABSTRACT The geographical regions of northwestern Mexico consisting of the Pacific Ocean, the Baja California Peninsula with its mountain range along it, the Gulf of California, and the coastal zone with its Western Sierra Madre Mountain range, configure an alternation of water, land, water, land, all interacting with the atmosphere. It suggests investigating the cloud patterns, what clouds are most relevant in controlling radiation and the climate, and what type of cloud is associated with observed precipitation patterns in the region. The principal aim was to carry out a climatology of five types of clouds: cumulonimbus, cumulus, altostratus, stratocumulus and nimbostratus. The data set was obtained from the MODIS sensor, placed on the Aqua and Terra satellites, covering the period from 2001 to 2020. The results revealed that the precipitation distribution predominantly relates to the deep convective cloud pattern. A minor fraction of rain is associated with the nimbostratus cloud pattern. The pattern of the fraction of total coverage of the five types of clouds coincides very well with lower radiation values, demonstrating the regulatory role of clouds in the area’s climatology.

Triple-Dip La Niña Contributes to Pakistan Flooding and Southern China Drought in Summer 2022

Abstract In the summer of 2022, a long-lasting La Niña entered its third year. In Asia, southern China was in the grip of a historic drought while heavy rainfall ravaged Pakistan. Using a climate model forced by observed sea surface temperatures (SST) over the equatorial Pacific, we show that the back-to-back La Niña events from 2020 to 2022 are a key contributor to the global SST pattern in 2022, including the negative-phase Pacific decadal oscillation and exceptionally strong negative Indian Ocean dipole. The model reproduces the observed precipitation pattern over South and East Asia, including enhanced rainfall over Pakistan–northwest India and reduced rainfall over southern China. Additional model simulations indicate that the negative Indian Ocean dipole combined with La Niña reduces southern China rainfall by causing anomalous subsidence and anticyclonic flows. These results highlight the dominant role of long-lasting La Niña in modulating rainfall over heavily populated monsoon Asia.

Barite and gypsum precipitation in chalk: A numerical simulation approach revealing the coupled impact of physical and chemical heterogeneities in porous media

In studies relevant to the contaminant transport in geosphere, the evolution of local physicochemical processes imparts potential challenges in safety and critical assessment issues. The transport of such contaminants in a rock pore structure could occur under the advective or diffusive regime. Such transport over the spatial and temporal scale depends upon the coupling between physical properties of the pore structure (permeability, pore size distribution, porosity) and chemical properties of the dissolved ionic species (pH, ionic strength, and surface interactions). The nature of such interactions leads to the generation of local imbalances in pore solution and thereby enhances the dissolution and precipitation of minerals. To predict the occurrence of such processes over large spatial and temporal scales, it is imperative to demonstrate how these physical and chemical processes interact within the heterogeneous porous media. In this context, numerical simulations were conducted to model the results of experimental dataset investigating barite and gypsum precipitation in chalk. In the experimental approach, the selected two minerals are end-members of the sulfate-alkali family and exhibit large differences in the kinetic rate and solubility, and the reference chalk sample resembles heterogeneity in its pore structure. The numerical simulations revealed that at the 1D scale, it was possible to model the overall experimental observations at the boundary monitoring points such as chemistry evolution in reservoirs, porosity loss during barite and gypsum precipitation, the total amount of barite and gypsum formation in clogging zone and the threshold saturation index to initiate precipitation. However, these simulations could not validate the experimentally observed impact of barite and gypsum clogging on the changes in water tracer transport. The underlying reason for such behavior was the formation of a unique clogging zone due to barite and gypsum precipitation. We showed that the 2D simulations incorporating spatial heterogeneity were able to reproduce the observed precipitation patterns, for both of the barite and gypsum experiments. Through 1D and 2D numerical results, we demonstrate the capability of a reactive transport model to validate the experimentally observed barite and gypsum precipitation behavior in chalk samples. Furthermore, it also highlights the interplays between the physicochemical heterogeneities, mineral reactivity and the transport rates that are responsible for the distinct precipitation pattern of the barite and gypsum in chalk samples, and consequently their impacts on transport properties.

Modeling of an Extreme Flooding Event in the Amazon Basin Using the WRF-Hydro Model

In the Amazon, the frequency of extreme events has been increasing notably in recent decades. In April, May, and June 2021, the city of Manaus faced the greatest flood in 119 years, and the Rio Negro reached at a level of 29.98 m. Because of this, the present work aims to evaluate the performance of the WRF-Hydro model in simulating precipitation during an extreme flood event in the Amazon Basin. The simulations were performed with 1 km spatial resolution and a 250 m channel network from April 30 to June 5, 2021. Such applications were evaluated using comparisons of the variability of accumulated precipitation with observed data from National Water Agency rainfall stations. The results showed a tendency for the model to underestimate the accumulated precipitation, slightly reproducing some observed precipitation patterns. We concluded that the tool presents a capacity for precipitation estimation, with potential for operational purposes.

Analysis of decadal precipitation changes at the northern edge of the Alps

Precipitation data from four weather stations in Bavaria, Germany, situated at the northern edge of the Alps have been evaluated for the period 1901 to 2019. Decadal changes have been computed as sliding 30‑year averages and as the difference between the 30‑year periods 1990–2019 and 1901–1930. The annual precipitation at these four stations increases, fitting in magnitude roughly to the temperature increase in the course of global warming. The number of dry days slightly increases as well as a consequence of a northward shift of the storm tracks over Europe. Due to these two features the precipitation intensity on wet days increases. The increase in maximum daily precipitation amounts is less than the increase in the annual precipitation amount. The observed maximum daily precipitation in the full four datasets is 138.5 mm at the station Hohenpeisenberg in 1999. The length of dry spells is increasing at three of the four stations as well. The overall observed precipitation patterns fit to the findings of regional climate model simulations for future climate scenarios for this region. Thus, they can be interpreted as signs that climate change is well underway in Southern Bavaria as it is in most places of the world.

A Reaction–Diffusion–Reaction System for Forming Periodic Precipitation Bands of Cu-Fe-Based Prussian Blue Analogues

We propose a simple and novel system to form precipitation patterns of Cu-Fe-based Prussian blue analogues (Cu-Fe PBA) in agarose gel through coupled electrochemical reactions, reactant ion diffusion influenced by electric field, and precipitation reactions. The spatiotemporal evolution, spatial distribution, and crystallite morphologies of the precipitates were investigated by visual inspection, Fe Kα intensity distribution measurements, and optical and scanning electron microscope observations. The observed precipitation patterns and their evolution depended on the applied voltage. Multicolored periodic precipitation bands were stochastically formed under cyclic alternating voltage (4 V for 1 h and then 1 V for 4 h per cycle). The distances between adjacent bands were randomly distributed (0.30 ± 0.25 mm). The sizes and shapes of the crystallites generated in the gel were position-dependent. Cubic but fairly irregular crystallites (0.1–0.8 μm) were formed in the periodic bands, whereas definitely cube-shaped crystallites (1–3 μm) appeared close to the anode. These cube-like reddish–brown crystallites were assigned to Cu-FeII PBA. In some periodic bands, plate-like blue crystallites (assigned to Cu(OH)2) were also present. Future issues for potential applications of the observed periodic banding for selective preparation of Cu-Fe PBA crystallites were discussed.

Minimizing pest and disease risks in uncertain climates: CABI initiatives developing new technologies and tools for outreaching early warning to farmers.

Abstract Recent climate models have projected a global temperature increase of at least 1.5-2°C on present day temperatures, including a high likelihood of an increase in extreme temperatures experienced in inhabited places. Changes to observed precipitation patterns are likely with several regions predicted to experience an increase in heavy precipitation and others likely to experience more drought, and a precipitation deficit. There is a growing body of evidence to suggest these changing climatic conditions affect the distribution and phenology of pests and diseases of agriculture. As ectothermic organisms, arthropod pests and fungal diseases are sensitive to changes in mean temperatures and temperature ranges to which they are exposed, with warmer average conditions likely to increase development rates and more extreme temperatures likely to have a negative impact on development. Models which incorporate climate change projections indicate that pests and diseases may shift or increase their ranges in line with warming temperatures. Smallholder farmers are particularly vulnerable to the effects of climate change, with farmers reporting negative impacts on crop production and needing to change farming practices in response to changing climates. In the face of changeable conditions, traditional pest management practices may not be as effective given the climate change induced changes which may be present in the ecosystems e.g. alteration of tritrophic interactions between pest and natural enemies. This paper gives an overview of current and recent projects where CABI and partners have developed and utilised existing technologies, methodologies and approaches that may help smallholder farmers to receive the necessary information to control pests and diseases in the context of changing climates. First, we cover the application of species distribution models and their benefits in highlighting areas at risk of pest incursion in the future. Second, we cover within season modelling approaches, driven by earth observation data sources to help farmers to make informed decisions on the best time to apply an intervention, in the face of changing within season temperatures. Finally, we report on CABI's activities associated with climate smart agriculture in South East Asia.

Observed precipitation pattern changes and potential runoff generation capacity from 1961–2016 in the upper reaches of the Hanjiang River Basin, China

The upper reaches of the Hanjiang River Basin (HM-CSNWTP) is the headwater source for the middle route of China's South-to-North Water Transfer Project (M-CSNWTP), as well as the north-south transitional zone of central China, and is characterized by high precipitation sensitivity and variability. Changes in precipitation patterns can affect the potential runoff generation capacity of the HM-CSNWTP. Therefore, this study was focused on exploring the dynamic precipitation patterns over the HM-CSNWTP and the potential runoff generation capacity of this region at annual and seasonal timescales. Daily precipitation data from 34 meteorological stations for the period 1961–2016 were collected in order to estimate 4 categories of rainfall indices: average daily precipitation on all days (ADP) and on rainy days (ARP), total amount (TP), and rainy days (TD). The non-parametric Mann-Kendall test, LOWESS smoothing, and collaborative kriging method were implemented. The statistical results indicated that: (1) The annual ADP over the basin decreased slightly, at the rate of −0.22%/10a (−0.002 mm/d/a). Seasonally, summer ADP increased by 1.4%/10a (0.005 mm/d/a), while decreasing ADP values were detected in other seasons. Large increases in annual and summer ARP were found, at rates of 3.73%/10a (0.018 mm/d/a) and 3.72%/10a (0.039 mm/d/a), over the HM-CSNWTP, mainly in the southwestern area (Region III), due to the large changes of annual and summer ARP. Annual and summer ARP increased at significant rates of 7.22%/10a (0.034 mm/d/a) and 10.64%/10a (0.083 mm/d/a), respectively. (2) Decreasing light rainfall amounts (TP0-10) and days (TD0-10), and increasing heavy rainfall amounts (TP>25) and days (TD>25) were detected over the HM-CSNWTP, especially for Region III, in which TP>25 and TD>25 increased by 7.00%/10a (1.076 mm/a) and 5.37%/10a (0.023 d/a), respectively. (3) In the wettest assumption scenario, the estimated annual, spring, summer, autumn, and winter runoff depths were 912.9 mm, 183.0 mm, 410.6 mm, 259.0 mm, and 60.3 mm, respectively. In the driest assumption, the estimated annual, spring, summer, autumn, and winter runoff depths were 131.4 mm, 35.6 mm, 65.7 mm, 27.8 mm, and 2.3 mm, respectively, and the planned diversion water accounted for 76.1%, 63.2%, 70.0%, 102.2%, and 156.5% of the annual and seasonal driest assumption runoff depths, respectively. Thus, the water diversion of the M-CSNWTP in the driest assumption may trigger water shortages and environmental disasters in the lower reaches of the Hanjiang River Basin, especially at annual, autumn, and winter timescales. These findings provide some basic information for local water resource management in the HM-CSNWTP.

Can Convection‐Permitting Modeling Provide Decent Precipitation for Offline High‐Resolution Snowpack Simulations Over Mountains?

AbstractAccurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.

Non-stationary spatial regression for modelling monthly precipitation in Germany

It is widely accepted that spatial dependencies have to be acknowledged appropriately in data that are spatially aligned. However, most spatial models still assume that the dependence structure does not vary over space, i.e., it is stationary. While assuming stationarity considerably facilitates estimation, it is often too restrictive when describing atmospheric phenomena such as precipitation. Nonetheless, the applicability of non-stationary models is often hindered, as their use reveals to be cumbersome and improvements over stationary models can be hard to identify. The stochastic partial differential equation (SPDE) approach to spatial modelling allows for flexible specifications of non-stationary models. In particular, given the German orographic diversity, it makes sense to investigate potential non-stationarity in precipitation patterns. Taking such potential non-stationarities into account may, in particular, leads to improved smoothing of the observed precipitation pattern taken on a finite set of measurement stations, and therefore to improved inputs to hydrological models. We suggest an SPDE-based model where the mean and dependence structure are allowed to vary with elevation, as well as a more flexible non-parametric alternative based on multivariate B-Splines. As factors such as wind may cause higher dependence in a given direction, we include anisotropy in the model. Results show that, according to the widely applicable Bayesian information criterion, a non-stationary model provides a better fit to the data. Taking German monthly precipitation as a motivation, we set up a simulation study to explore the ability of the elevation and spline-based models to correctly identify non-stationarity.

Developing climate model ensembles: A comparative case study

Projections of future changes in hydroclimatic variables are available through various general circulation models (GCMs) that are part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Assessment of the performance of these models in simulating past climate, both individually and as an ensemble, has received much attention and is commonly the first step in assessing their suitability of application. In this study, we have evaluated the ability of various ensemble models to simulate past temperature and precipitation in the Gulf Basin region of North America, chosen as an illustrative case study. We have developed ensembles from 34 CMIP5 GCMs using six diverse approaches, including random forest, support vector regression, neural networks, linear regression, and weighted k-nearest neighbors, and compared the performance of the ensembles with each other and the individual GCMs using a robust set of metrics and nonparametric tests. For temperature, random forest outperforms all other ensembles and the best-performing GCM by a statistically significant margin and is able to simulate temporal and spatial patterns in temperature well. None of the ensembles are able to adequately simulate observed precipitation patterns in the study area, likely due to spatial differences in precipitation drivers in the region, as well as the coarseness of the dataset itself. However, the random forest, support vector regression, neural network, and linear regression ensembles achieved statistically significant improvements to precipitation simulation, as compared to the individual GCMs and the simple arithmetic mean ensemble, which has been used in several studies.

Effects of cumulus parameterization closures on simulations of summer precipitation over the United States coastal oceans

AbstractThis study evaluates the effects of major cumulus parameterization closures on summer precipitation simulations over the U.S. Atlantic Coasts and Gulf of Mexico. A series of mesoscale regional climate model simulations using an Ensemble Cumulus Parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation are conducted and compared to determine the systematic errors and relative performances of individual and combined closures in capturing precipitation spatiotemporal variations. The results show that closure algorithms largely affect precipitation's geographic distribution, frequency and intensity, and diurnal cycle. The quasi‐equilibrium and total instability adjustment closures simulate widespread wet biases, while the instability tendency closure produces systematic dry biases. Two closure algorithms based on the average vertical velocity at the cloud base and column moisture convergence complementarily reproduce the observed precipitation pattern and amount, and capture the frequency of heavy rainfall events better than other closures. In contrast, the instability tendency closures are better at capturing the diurnal phase but yield much larger deficits in amount. Therefore, cloud base vertical velocity and moisture convergence may be the primary factors controlling precipitation seasonal mean and daily variation, while the instability tendency may play a critical role in regulating the diurnal cycle phase.

Mechanisms producing different precipitation patterns over north‐eastern Italy: insights from HyMeX‐SOP1 and previous events

During the first HyMeX Special Observation Period (SOP1) field campaign, the target site of north‐eastern Italy (NEI) experienced a large amount of precipitation, locally exceeding the climatological values and distributed among several heavy‐rainfall episodes. In particular, two events that occurred during the last period of the campaign drew our attention. These events had common large‐scale patterns and a similar mesoscale setting, characterised by southerly low‐level flow interacting with the Alpine orography, but the precipitation distribution was very different. During Intensive Observing Period IOP18 (31 October–1 November 2012), convective systems were responsible for intense rainfall mainly located over a flat area of the eastern Po Valley, well upstream of the orography. Conversely, during IOP19 (4/5 November 2012), heavy precipitation affected only the Alpine area. In addition to IOP18 and IOP19, the present study analyses other heavy‐precipitation episodes that display similar characteristics and which occurred over NEI during the autumn of recent years. A high‐resolution (2 km grid spacing) non‐hydrostatic NWP model and available observations are used for this purpose.The two different observed precipitation patterns are explained in terms of interaction between the impinging flow and the Alps. Depending on the thermodynamic profile, convection can be triggered when the impinging flow is forced to rise over a pre‐existing cold‐air layer at the base of the orography. In this situation a persistent blocked‐flow condition and upstream convergence are responsible for heavy rain localized over the plain. Conversely, if convection does not develop, flow‐over conditions are established and heavy rain affects the Alps. Numerical parameters proposed in the literature are used to support the analysis.Finally, the role of evaporative cooling beneath the convective systems is evaluated. It turns out that the stationarity of the systems upstream of the Alps is mainly attributable to persistent blocked‐flow conditions, while convective outflow slightly modifies the location of precipitation.

Impact of topography on the diurnal cycle of summertime moist convection in idealized simulations

The impact of an isolated mesoscale mountain on the diurnal cycle of moist convection and its spatial variation is investigated. Convection-resolving simulations of flow over 3D Gaussian-shaped mountains are performed for a conditionally unstable atmosphere under diurnal radiative forcing. The results show considerable spatial variability in terms of timing and amount of convective precipitation. This variability relates to different physical mechanisms responsible for convection initiation in different parts of the domain. During the late morning, the mass convergence from the radiatively driven diurnal upslope flow confronting the large-scale incident background flow triggers strong convective precipitation over the mountain lee slope. As a consequence, instabilities in the boundary layer are swept out by the emerging cold pool in the vicinity of the mountain, and some parts over the mountain near-field receive less rainfall than the far-field. Over the latter, an unperturbed boundary-layer growth allows for sporadic convective initiation. Still, secondary convection triggered over the leading edge of the cold pool spreads some precipitation over the downstream near-field. Detailed analysis of our control simulation provides further explanation of this frequently observed precipitation pattern over mountains and adjacent plains. Sensitivity experiments indicate a significant influence of the mountain height on the precipitation pattern over the domain.

Precipitation diagnostics of an exceptionally dry event in São Paulo, Brazil

The State of Sao Paulo in Brazil experienced in 2014 and early 2015 an expressive precipitation deficit, leading to drought conditions with impacts in water availability for public consumption, hydropower generation, and agriculture, particularly during austral summer. This study performs a detailed diagnostics of the observed precipitation during 2014 and early 2015 over a particular region of Sao Paulo State, which includes the massively populated metropolitan region of Sao Paulo. The diagnostic was designed to provide answers to a number of relevant questions for the activities, decisions, and strategic planning of several sectors (e.g., general public, media, and high-level governments). Examples of questions such diagnostics can help answer are: How much precipitation has the region received? Has the region experienced drought conditions in the past? When have similar drought conditions been observed in the past? What has been the observed precipitation pattern in the last years? How severe/rare were the 2014 and 2015 droughts? When does the rainy season typically start/end in the region? What happened during the 2013/2014 and 2014/2015 rainy seasons? The performed diagnostics based on historical 1961/1962–2014/2015 records revealed that the 2013/2014 austral summer was a very rare event classified as exceptionally dry. Similar drought events were previously recorded but with smaller magnitude in terms of precipitation deficits, making the 2013/2014 drought event the driest on the examined record. In fact, the region has been experiencing a precipitation deficit pattern since 1999/2000. One of the contributing factors for the expressive precipitation deficit in 2014 was the abnormally early end of the 2013/2014 rainy season in the region.

A glacial zephyr

The hydrology of the North American west looked very different at the Last Glacial Maximum to today. A model–data comparison suggests the observed precipitation patterns are best explained if the storm track was squeezed and steered by high-pressure systems.

CMIP5 Historical 시나리오에 근거한 WRF를 이용한 한반도 중심의 동북아시아 상세기후

In this study, climate over Korea based on the Historical scenario induced by HadGEM2-AO is simulated by WRF. For this purpose, a system that can be used be for numerical integration over the Far East Asian area of the center of the Korean Peninsula with 12.5 km-horizontal resolution was set-up at "Haebit", the early portion of KMA Supercomputer Unit-3. Using the system, the downscaling experiments were conducted for the period 1979-2010. The simulated results of HadGEM2-AO and WRF are presented in terms of 2 m-temperature and precipitation during boreal summer and winter of Historical for the period 1981~2005, compared with observation. As for the mean 2 m-temperature, the general patterns of HadGEM2-AO and WRF are similar with observation although WRF showed lower values than observation due to the systematic bias. WRF reproduced a feature of the terrain-following characteristics reasonably well owing to the increased horizontal resolution. Both of the models simulated the observed precipitation pattern for DJF than JJA reasonably, while the rainfall over the Korean Peninsula in JJA is less than observation. HadGEM2-AO in DJF 2 m-temperature and JJA precipitation has warm and dry biases over the Korean Peninsula, respectively. WRF showed cold bias over JJA 2 m-temperature and wet bias over DJF precipitation. The larger bias in WRF was attributed to the addition of HadGEM2-AO's bias to WRF's systematic bias. Spatial correlation analysis revealed that HadGEM2-AO and WRF had above 0.8 correlation coefficients except for JJA precipitation. In the EOF analysis, both models results explained basically same phase changes and variation as observation. Despite the difference in mean and bias fields for both models, the variabilities of the two models were almost similar with observation in many respects, implying that the downscaled results can be effectively used for the study of regional climate around the Korean Peninsula.