National Centers For Environmental Prediction/National Center For Atmospheric Research Research Articles

Study of the Sea Temperature Backgrounds to Tropical Cyclones Affecting Hainan Province in the Dry Season

Using the best path data set of tropical cyclones from the China Meteorological Administration, NCEP/NCAR (National Centers for Environmental Prediction–National Center for Atmospheric Research) reanalysis data, JMA (Japan Meteorological Agency) SST (surface sea temperature) data and NCEP subsurface sea temperature data, the sea temperature background of tropical cyclones affecting Hainan Province in the dry season was analyzed using typical cases from November to December. The results show that there was a significant positive correlation between the number of tropical cyclones affecting Hainan Province and the sea surface temperature in the South China Sea and the Western Pacific. The high SST in the South China Sea and Northwest Pacific were favorable for the occurrence and development of tropical cyclones. The circulation situation also had a significant impact on tropical cyclones during the concurrent period. The wind field convergence was conducive to the occurrence and development of tropical cyclones. The high subsurface sea temperature in the Western Pacific at the depth of 5–50 m was conducive to the strengthening of convection in the Pacific warm pool and the occurrence of the tropical cyclones. The typical cases of 1993 and 2017 have effectively verified the impact of the sea surface temperature background and circulation situation on tropical cyclones affecting the Hainan Province from November to December.

Impact of solar variability on Indian summer monsoon through large scale circulations

Via large-scale circulations, the role of solar variability on the Indian summer monsoon is investigated. Standardized anomaly is used to identify years of solar maxima and minima. Statistical analysis such as moving mean, empirical mode decomposition, and wavelet analysis are used to determine the plausible relationship between solar variability, large scale circulations such as El- Niño 3.4 SST anomaly, Dipole Mode Index (DMI), and Atlantic Multidecadal Oscillation (AMO), and Indian summer monsoon rainfall (ISMR). In addition, the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP-NCAR) reanalysis data collection is used to assess time-averaged composite anomalies of wind, latent heat, geopotential height during maximum/minimum solar activity years.The 31-years moving mean of DMI shows significant negative correlation (−0.35) with sunspot number and with 31-years moving mean ISMR (−0.28). Similarly, 31-years moving mean of AMO exhibits a substantial positive connection with ISMR (0.68) and Niño 3.4 SST shows significant negative correlation (−0.62) with ISMR. Using intrusive mode function analyses it has noted that India receives rainfall for both phase of solar forcing but from wind studies at 850 hPa and 200 hPa, it is found that the phenomena for ISMR is different during solar maximum and minimum conditions. During solar maximum, a strong Low Level Jet is observed, and during solar minimum, a strong Tropical Easterly Jet is observed. Also, the area of influences of ISMR is different for solar maximum and solar minimum. The mean difference plot of seasonal rainfall shows that during solar maximum, north India receives reasonable amount of rainfall however during solar minimum, south India receives comparatively more rainfall than north India. A weakening of the local Hadley cell during solar minima is also observed.

Regional Characteristics of Summer Precipitation Anomalies in the Northeastern Maritime Continent

Based on the monthly mean reanalysis data from NCEP/NCAR (National Centers for Environmental Prediction/ National Center for Atmospheric Research) and GPCP (Global Precipitation Climatology Project) (1979–2020), the regional characteristics of precipitation in the warm pool side of the Maritime Continent (MC) and the relationships between different precipitation patterns and atmospheric circulations are studied. The results show that there are significant correlations as well as differences between the precipitation in the east of the Philippines (area A) and that in the Pacific Ocean near the Northern Mariana Islands (area B). Precipitation in area A is closely related to the eastern Pacific ENSO (El Nino-Southern Oscillation) and EAP/PJ (East Asia-Pacific/Pacific-Japan) teleconnection pattern, while precipitation in area B is linked to the Indian Ocean basin-wide and the South China Sea summer monsoon. When the precipitation anomaly in area A is positive, the East Asian summer monsoon is weak. A cyclone appears to the northwest of area A at 850 hPa with convergence airflow. After filtering out the effects of precipitation in area B, the cyclone retreats to the west, and an anticyclone appears to the southeast of area A. When the precipitation is above normal in area B, the circulation and water vapor transportation are similar to that in area A but more to the east. The updraft and downdrafts to both north and south sides of area B form two closed meridional vertical circulations. When the influence of area A is moved out, the circulation center in the warm pool area moves eastward. This research contributes to a better understanding of the regional characteristics of the Maritime Continent and the East Asian summer monsoon.

Characteristics of Environmental Parameters of Compound and Single Type Severe Convection in Hunan

Based on the multi-source observation data and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data in Hunan from 2002 to 2021, this study statistically analyzes the climatic characteristics of compound severe convection. The distribution characteristics and prediction thresholds of environmental parameters of compound severe convective weather, thermal hail, thunderstorm gale and short-term heavy precipitation events in Hunan, are compared and analyzed. The results show that: (1) The primary type of compound severe convection is a thunderstorm gale that accompanies short-term heavy precipitation. The compound severe convection mainly occurs in spring and summer, with the most common occurrence in July. There are seasonal differences in the types of compound severe convection. The primary type in spring is a thunderstorm gale with hail and hail with short-term heavy precipitation. The primary type in summer is a thunderstorm gale with short-term heavy precipitation. The probability of compound severe convection is the highest in southeastern Hunan, and the lowest in southwestern Hunan. (2) The values of the critical physical parameters required for the occurrence of thermal hail, such as the thermal instability stratification, CAPE and 0–6 km vertical wind shear, are the highest, followed by compound severe convection. Compound severe convection also requires high water vapor, which is only inferior to short-term heavy precipitation. (3) The critical physical parameters also differ in spring and summer. For the temperature difference between 850 and 500 hPa, the seasonal difference is not apparent, indicating that all types of severe convective weather need strong convective instability. CAPE is generally higher in summer than in spring, while the surface-specific humidity and 0–6 km vertical wind shear are higher in summer than in spring.

Asymmetry variations in Arctic summer onset and ending: Role of sea-ice melting

Previous studies found that in Arctic regions with severe sea ice melting, summer ending (SE) variations are significantly larger than summer onset (SO) variations in the past few decades. Based on short-term observations, researchers preliminarily suggested that radiation variations caused by an earlier melting onset could be the possible reason for asymmetric Arctic SO/ending variations (AASV). Based on observations and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis I dataset, here we quantitatively characterize AASV by calculating the difference between the 11 year sliding trend of Arctic SO and SE. The results show that AASV positively correlates with sea ice melting in summer. The increased summer sea ice melting increases the area to absorb short-wave radiation in summer and then release more long-wave radiation to heat the lower atmosphere and delay the peak time of long-wave radiation releasing. The variations in radiation lead to a significant delay of the Arctic SE, with no significant variations in SO. We introduce CMIP6 historical and future simulations of 15 models to verify further the relationship between AASV and summer sea ice melting. Historical run reproduces the observed asymmetry, and future simulations under various warming levels show that AASV will vanish with disappeared melting variations or be strengthened with increased melting. The latter could delay freeze-up and further exacerbate the following years’ melting, which will enhance AASV. Furthermore, AASV will delay the onset and peak time of Arctic amplification.

2022: An Unprecedentedly Rainy Early Summer in Northeast China

In the early summer (June) of 2022, the spatial mean precipitation in northeast China (NEC) was 62% higher than normal and broke the historical record since 1951. Based on the precipitation data of 245 meteorological stations in NEC and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, this paper analyzes the role of large-scale circulation and sea-surface temperature (SST) associated with anomalous precipitation over NEC in June using singular value decomposition (SVD), correlation analysis, regression analysis, and composite analysis methods, and further investigates the possible cause of the abnormal precipitation in June 2022. Results show that the northeast China cold vortex (NCCV) accompanying the blocking high in the Okhotsk Sea (BHOS) has been the primary mid-to-high latitude atmospheric circulation pattern affecting NEC precipitation in June since 2001. This circulation pattern is closely related to the tripole SST pattern over the North Atlantic (NAT) in March. In June 2022, the NAT SST anomaly in March stimulates eastward-propagating wave energy, resulting in the downstream anomalous circulation pattern in which the NCCV cooperates with the BHOS in the mid-high latitudes of East Asia. Under this background atmospheric circulation favorable for precipitation, the Kuroshio region SST anomaly in June led to a more northward and stronger anomalous anticyclone in the northwestern Pacific through local air–sea interaction, which provides more sufficient water vapor for NEC, resulting in unprecedented precipitation in June 2022.

Impact of Atmospheric Circulation on the Occurrence of Very Strong and Extreme Cold Stress in Poland

Abstract The primary objective of the study was the determination of the spatial and multiannual variability of occurrence of days with very strong and extreme cold stress in Poland according to the Universal Thermal Climate Index (UTCI), as well as determination of baric conditions favouring their occurrence. The study was based on data from the years 1966/67 to 2018/19 from the Institute of Meteorology and Water Management – National Research Institute and National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR). The research showed a statistically significant decrease in the number of days with very strong and extreme cold stress on half of the analysed stations, and a slight tendency or no changes on the remaining stations. The occurrence of days with extreme cold stress in Poland, as revealed by the analysis was primarily related to the presence of high-pressure systems blocking zonal circulation. The study resulted in the designation of three circulation types, i.e. two types related to anticyclonic systems and one cyclonic type.

Analysis of Synergistic Effects of Cold Source and East Asian Winter Wind on Air Pollution in Typical Regions of China in Winter

This paper collects and analyzes the 1954–2017 monthly average reanalysis data from the U.S. National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), the 1954–2017 haze days observation data from the National Meteorological Information Center/China Meteorological Administration (NMIC/CMA) and the PM2.5 daily average mass concentration data in 2013–2017 from the China Air Quality Online Monitoring Platform. The atmospheric apparent heat source Q1 (negative Q1 means cold source in winter) over the Tibetan Plateau in December of 1954–2017 is estimated based on thermodynamic equations, and the East Asian winter monsoon index (EAWMI) is calculated. In addition, the discrepancies of the air quality among China’s five typical regions (Beijing–Tianjin–Hebei, Fen-Wei Plain, Yangtze River Delta, Pearl River Delta and Sichuan-Chongqing regions) under the joint influence of the Q1 and EAWMI are studied. Results show that when it is a strong cold source year, abnormal downdrafts and “temperature inversion covers” occur in areas far from the high terrain, resulting in increased pollution, while the opposite is true in weak cold source years. In strong EAWMI years, there is an abnormal northerly sinking cold flow in the lower layers of mid-high latitudes, which increases the pollution in the area south of 30° N, and the opposite is true in weak EAWMI years. Affected by the combined activities of the Q1 and the EAWMI, the meteorological conditions of the five typical regions are different, and thus present different air pollution characteristics.

Objective classification of weather types for the Eastern Siberia over the 1970–2020 period using the Jenkinson and Collison method

A daily catalogue of weather types based on the Jenkinson and Collison (JC) objective approach has been developed for the Eastern Siberia region of Russia (47.5°–67.5° N, 102.5°–132.5° E) during the period 1970–2020. The mean sea level pressure (MSLP) dataset from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis was used for the classification. A statistical analysis of the annual, seasonal and monthly distribution of 27 weather types has been accomplished. For the entire period, the most frequent weather types were found to be anticyclonic (A, 23.5%), followed by cyclonic (C, 11.5%), unclassified (U, 9.1%) and the pure advective types N (6.9%), NE (6.0%) and W (5.4%). The advective atmospheric patterns are predominated by northerly and westerly components, which tend to weaken in summer and strengthen in the winter and transitional seasons. The westerlies dominate in the cold half of the year (October–March), while the easterlies are prevailing in the warm season (April–September). The long-term trends in frequencies of the atmospheric circulation patterns over the study period have been analysed. There are statistically significant decreasing trends of anticyclonic weather types, while types with northerly and westerly components show increasing trends. The relationships between the atmospheric circulation patterns and surface precipitation were ascertained from 87 meteorological stations. Anticyclonic weather types are consistent with dry weather conditions in all seasons, while cyclonic and advective types with easterly component are correlated with wet weather conditions. The seasonal reversal of zonal airflow across the study area (the westerly flow in winter and the easterly in summer), accompanied by the wetter weather conditions for the easterly circulation patterns, suggests the influence of the East Asian monsoon. Regional climate changes of recent decades, such as accelerated glacier shrinkage in the 1990s, increasing the number of droughts, and rainfall variability are in good agreement with the revealed atmospheric circulation patterns.

Simulation and Evaluation of Statistical Downscaling of Regional Daily Precipitation over North China Based on Self-Organizing Maps

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10−4 and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China.

Influence of Sea Level Pressure on Inter-Annual Rainfall Variability in Northern Senegal in the Context of Climate Change

This study examines the inter-annual variability of rainfall and Mean Sea Level Pressure (MSLP) over west Africa based on analysis of the Global Precipitation Climatology Project (GPCP) and National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis respectively. An interconnection is found in this region, between Mean Sea Level Pressure (MSLP) anomaly (over Azores and St. Helena High) and monthly mean precipitation during summer (June to September: JJAS). We also found that over northern Senegal (15°N - 17°N; 17°W - 13°W) the SLP to the north is strong; the wind converges at 200 hPa corresponding to the position of the African Easterly Jet (AEJ) the rotational wind 700 hPa (corresponding to the position of the African Easterly Jet (AEJ) coming from the north-east is negative. In this region, the precipitation is related to the SLP to the north with the opposite sign. The Empirical Orthogonal Functions (EOF) of SLP is also presented, including the mean spectrum of precipitation and pressures to the north (15°N - 40°N and 50°W - 25°W) and south (40°S - 10°S and 40°W - 0°E). The dominant EOF of Sea Level Pressures north and south of the Atlantic Ocean for GPCP represents about 62.2% and 69.4% of the variance, respectively. The second and third EOFs of the pressure to the north account for 24.0% and 6.5% respectively. The second and third EOFs of the pressure to the south represent 12.5% and 8.9% respectively. Wet years in the north of Senegal were associated with anomalous low-pressure areas over the north Atlantic Ocean as opposed to the dry years which exhibited an anomalous high-pressure area in the same region. On the other hand, over the South Atlantic, an opposition is noted. The wavelet analysis method is applied to the SLP showings to the north, south and precipitation in our study area. The indices prove to be very consistent, especially during intervals of high variance.

Mode of Wind and Sea Surface Temperature Over the South China Sea During Rainy Season in Indonesia

Research on near-surface wind and sea surface temperature dynamics in the South China Sea (SCS) is important to understand their impact on Indonesia’s rainy season variations. In this study, we use daily wind data from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and sea surface temperature (SST) from the Optimal Interpolated Sea Surface Temperature (OISST) during November-to-March (NDJFM) period of 2000-2016 to explore the dominant signal of temporal and spatial variations in the SCS. Results of the first EOF (EOF-1) show that the SST cooling occurred in the entire region (the South China Sea, local Indonesian sea, and the Indian Ocean nearly Sumatra), thus, is remarked as “Cold Tongue” (CT). Additionally, the CT coincides with the strengthening of northerly wind over the South China Sea (SCS) elongated in the equator’s vicinity, which is indicated as a cross-equatorial north wind (CENS). For meridional wind, the spatial pattern of the EOF-1 (17.75%) shows that the strength of this CENS is associated with SCS-type (remarked as South China Sea type) of Cold Surge (CS) pathway. This CS SCS-type could enhance the Asian winter monsoon flows over Indonesia. On the other hand, the spatial pattern of the second EOF (EOF-2) shows the enhancement of the northerly wind towards the Philippine Sea (12.65%) associated with PHP-type (remarked as Filipina sea type) of CS pathway (CS PHP-type). The CS PHP-type has a relationship with strong CT (12.65%). The CT, which is observed extending southward close to the Karimata Strait, appeared not to relate to the local CENS, which occurred early. Regarding the EOF-1 results, it appeared that the correlation between northerly wind and SST in the whole of the Maritime Continent region. It also shows that the CT developed from mid-December to mid-January. On the other side, the CENS occurred from mid-December to last-February.

The QBO Modulation on CO Distribution in the UTLS Over the Asian Monsoon Region During Boreal Summer

Using the Microwave Limb Sounding measurements and the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis product, we have investigated the response of carbon monoxide (CO) mixing ratio within the Asian Summer Monsoon Anticyclone (ASMA) region to the Quasi-Biennial Oscillation (QBO) and the potential dynamical mechanisms behind it. The results showed that the mixing ratio of CO within the ASMA is significantly higher at both 100 hPa and 147 hPa in the east phase of QBO (EQBO) years than in the west phase of QBO (WQBO) years, especially in the eastern part of the ASMA. The CO difference between EQBO and WQBO years is consistent with the interannual variation of the location and strength of the ASMA. Specifically, during the EQBO years, the area of the ASMA reduces to the southeast in the upper troposphere, while the westerly jet on the north side of the anticyclone and the southerly winds on the west side of the anticyclone both strengthen. These variations are associated with tropospheric circulation anomalies. The ascending motions over the Tibetan Plateau (TP) are enhanced during the EQBO years, which are correlated with the relatively high CO at 100 hPa. Consistently, an anomalous cyclonic circulation is found at 500 hPa over the TP when the QBO is on its east phase, which promotes the convergence of air mass and carries CO from the surface to the Upper Troposphere and Lower Stratosphere (UTLS).

Wintertime Cold Extremes in Northeast China and Their Linkage with Sea Ice in Barents-Kara Seas

The impacts of Arctic sea ice on the interannual variability of winter extreme low temperature (WELT) in Northeast China (NEC) and the associated atmospheric circulation patterns are explored in this study based on meteorological observation and the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. Results show that WELT in NEC has prominent interannual variability. We further use ±0.8 standard deviation as the threshold to select the years of frequent and rare extreme low temperature anomalies. Using composite analysis, we find that there are significant negative geopotential height anomalies at 500 hPa over NEC and positive geopotential height anomalies along the Arctic region, which represent the intensification of the East Asian trough (EAT) and the negative Arctic Oscillation (AO) phase in the years of more frequent WELT. The opposite characteristics are detected in the years of rare WELT. Furthermore, we determine that the Barents-Kara Seas are key sea ice regions in Arctic area. In the years of frequent WELT, the decrease of autumn Barents-Kara Seas sea ice and the positive sea surface temperature anomaly can last until the following winter, which is conducive to the intensification of anticyclonic anomalies in Ural regions and the northward extension of Ural ridge (UR). The northerly flow in front of UR guides the cold air penetrating southward from polar regions. Moreover, the anomalous cyclone over East Asia deepens the EAT. The northerly wind behind EAT guides the cold air to the NEC region, causing the wintertime low temperature there. The almost opposite situation occurs in the years of rare WELT.

Performance evaluation of NCEP/NCAR reanalysis blended with observation-based datasets for estimating reference evapotranspiration across Iran

Computing crop reference evapotranspiration (ETo) with the FAO Penman-Monteith method (PM-ETo) requires maximum and minimum air temperature, shortwave radiation, relative air humidity, and wind speed that are often unavailable in many places. The use of reanalysis data, which is common in climate studies, represents an alternative to observation data for the mentioned weather variables when are not available. This study focuses on the use of the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR)reanalysis blended with gridded data sets for computing monthly PM-ETo at 43 synoptic stations distributed across Iran. First, with a set of statistical indicators, the reanalysis weather variables required for computing ETo were compared with observation data, where a good match was obtained for solar radiation, maximum temperature, and minimum temperature. In contrast, at most of the studied stations, the reanalysis wind speed and relative humidity showed large errors when compared with observations. The PM-ETo computed with the blended reanalysis data were also compared with those obtained using observations. The results show that the PM-ETo computed from blended reanalysis compares relatively well with the ETo computedfrom observation at most of the studied stations. However, the spatial pattern of performance indicators reveal extremely poor results for the coastal locations of the Caspian Sea in the north and the Persian Gulf and Oman Sea in the south of Iran. Results suggest that, except for wind speed and relative humidity, the blended reanalysis products are suitable forestimatingETo in inland areas of Iran despitesome degree of overestimationat most of the studied stations.

Large-scale atmospheric circulation patterns associated with extreme monsoon precipitation in Pakistan during 1981–2018

Deviations in South Asian Summer Monsoon (SASM) precipitation affect the regional floods and drought patterns. In the current study, in-situ observations from Pakistan Meteorological Department (PMD), remotely sensed precipitation data from Climate Hazard Infrared Precipitation with Station data (CHIRPS), reanalysis data from ERA5, and National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) during 1981–2018 are used to explore the atmospheric circulation patterns during above and below-normal precipitation episodes. In a statistical sense, two methods, namely the Empirical Orthogonal Function (EOF) and Percent Normal (PN) indices are used to derive dominant spatial patterns and temporal evolution of extreme monsoon precipitation episodes. Results inferred 60% of the variance to the leading EOF mode depicting a similar spatial pattern of eigenvectors across the region. The leading principal component (PC) and PN index together depicted similar deviations, complementing the extreme flooding and drought years. From the composites, an anomalous increase (decrease) in seasonal precipitation magnitude was observed. The possible mechanism suggests an active control of atmospheric and sea-surface temperature (SST) forcing by altering the wind ascent (descent). The jet streams (200 hPa) intensification of Rossby waves high (low) pressure provides favorable frontal boundaries between polar cold and tropical warm air masses. The westerlies and easterlies are intensified (suppressed) during the above (below) normal precipitation composites, affecting the moisture transport. The enhanced (reduced) convective activities in the Indian Ocean as a primary source affected precipitation in the region during each composite.

Relationships between Large-Scale Climate Signals and Winter Precipitation Amounts and Patterns over Iran

Precipitation is one of the most complex weather phenomena; its successful spatiotemporal modeling provides substantial information for designing water management systems. This research aimed to determine the relationship of sixteen large-scale climate signals with weather types (WTs) as well as precipitation amounts over Iran during the winter season (January–March). Daily precipitation data covering the period 1991–2010 were collected from 130 weather stations across the country. In addition, four atmospheric variables affecting the precipitation process over Iran were derived from National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis datasets as predictors. Several nonhomogeneous hidden Markov models (NHMMs) were employed to identify the prevailing WTs governing the spatial distributions of daily precipitation in winter over Iran. A hidden state (i.e., WTs) in NHMMs was dependent on an immediately previous state of WT in the hidden layer and the reanalyzed atmospheric variables at the current time in the predictor layer. Daily WTs over the recording period were decoded using Viterbi’s algorithm. Results showed that the NHMM including the atmospheric variable of mean sea level pressure (Mslp) in the predictor layer had the minimum value (= 0) of Bayesian information criterion difference (BICD) among the other variables and was treated as the best model. The chosen model distinguished the eight spatially coherent WTs for winter that were in accordance with the eight synoptic patterns influencing the study area. Based on the Spearman’s rank method, amounts of winter precipitation at many stations in Iran were significantly correlated with four climate signals, including the atlantic multidecadal oscillation (AMO), East Atlantic/West Russia (EA/WR) pattern, Southern Oscillation Index (SOI), and Trans Polar Index (TPI). However, these station-based relationships could not exhibit well the spatial dependencies of the teleconnections and regional precipitation. It was also found that the unconditional and conditional frequencies of WTs were under the influence of a greater number of large-scale climate signals than the precipitation amounts, were under the influence of a greater number of large-scale climate signals. Moreover, the inherent nature of WTs in keeping spatial dependencies allowed for a better understanding of regional teleconnective relationships. These findings confirmed the advantage of regional rather than local assessments of teleconnective relationships.

Downscaling diurnal temperature over west and southwest Iran : A comparison of statistical downscaling approaches

This study aimed to forecast temperature variations in the western and southwestern part of Iran using a general circulation model and artificial neural networks (ANN). The data included mean diurnal temperatures from synoptic stations, National Centers for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis data, and outputs of a third-generation global climate model, the Hadley Centre Coupled Model version 3 (HadCM3), under A2 and B2 scenarios for the baseline period (1961–1990). The data of the first (1961–1975) and second 15 years (1976–1990) of the baseline period were used for model calibration and validation, respectively. Both models, however, produced reliable estimates at the plain stations with neither outperforming the other due to their negligible errors. However, the neural network results of mountain synoptic stations showed a lower error rate than the statistical downscaling model (SDSM) outputs. All in all, we can say that there was a larger amount of error in the outputs of the atmospheric general circulation models (AGCMs) in the mountainous regions. According to the outputs of the neural network and the AGCMs, temperatures at the studied stations were on the rise. In fact, this increase was more noticeable at the plain stations. This can be attributed to their proximity to the sea, to their latitude, and to the more intensive industrial activities (especially, extraction of petroleum and production of petroleum products) taking place near the plain stations.

Examination of Various Feature Selection Approaches for Daily Precipitation Downscaling in Different Climates

To turn General Circulation Models (GCMs) projection toward better assessment, it is crucial to employ a downscaling process to get more reliability of their outputs. The data-driven based downscaling techniques recently have been used widely, and predictor selection is usually considered as the main challenge in these methods. Hence, this study aims to examine the most common approaches of feature selection in the downscaling of daily rainfall in two different climates in Iran. So, the measured daily rainfall and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) predictors were collected, and Support Vector Machine (SVM) was considered as downscaling methods. Also, a complete set of comparative tests considering all dimensions was employed to identify the best subset of predictors. Results indicated that the skill of various selection methods in different tests is significantly different. Despite a few partial superiorities viewed between selection models, they not presented an obvious distinction. However, regarding all related factors, it may be deduced that the Stepwise Regression Analysis (SRA) and Bayesian Model Averaging (BMA) are better than others. Also, the finding of this study showed that there are some weaknesses in the interpretation of SRA, so concerning this issue, it may be concluded that BMA has more reliable performance. Furthermore, results indicated that generally, the downscaling procedure has more accuracy in arid climate than cold-semi arid climate.

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

AbstractDifferent multi‐model ensemble (MME) methods were investigated for their potential to improve the skill of 1‐month lead seasonal forecast products, based on six models from Global Producing Centers (GPCs) for long‐range forecasts (LRFs) designated by the World Meteorological Organization (WMO). We first compared the hindcast performance of seven MME methods (simple composite method, SCM; simple linear regression, SLR; multiple linear regression, MLR; best selection anomaly, BSA; multilayer perceptron, MLP; radial basis function, RBF; genetic algorithm, GA) for the global 2‐m temperature and precipitation for 1983–2009. The reference datasets for 2‐m temperature and precipitation are the ERA‐Interim from European Centre for Medium‐Range Weather Forecasts (ECMWF) and Global Precipitation Climatology Project (GPCP) for hindcast verification. For real‐time verification, the data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis 1 for 2‐m temperature and climate anomaly monitoring system and outgoing longwave radiation precipitation index (CAMS OPI) for precipitation are used. According to our analysis, GA was the most successful MME method in predicting both the global 2‐m temperature and precipitation for all four seasons. GA also showed good performance in predicting the 2‐m temperature and precipitation over the 13 regional climate outlook forum (RCOF) regions in all four seasons, but the range in performance among the RCOF regions varied significantly. In a real‐time forecast period (MAM 2012‐DJF 2015/16), GA outperformed in terms of time‐averaged anomaly pattern correlation coefficient (ACC) and root‐mean‐square error (RMSE) of the 2‐m temperature, although the forecast skill difference (0.02) between GA and SCM was not statistically significant. For the precipitation, both SCM and GA also reveal better performance than other MME methods. During the very strong El Niño event in 2015, individual models show better performance than other years. Nonetheless, these two MME methods outperform all the individual models.