Atmospheric circulation types and winter daily precipitation in Iran

Raziei, Tayeb; Bordi, Isabella; Santos, João A.; Mofidi, Abbas

doi:10.1002/joc.3596

Cited by 40 publications

(22 citation statements)

References 46 publications

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“…This issue has been discussed in a number of recent studies (Zarenistanak et al 2014;Farajzadeh and Karimi 2014). While changes in precipitation type are not only influenced by variations in T d min , but also by many other factors (Raziei et al 2012(Raziei et al , 2013Ghasemi and Khalili 2006), the important role of changes in T d min in altering precipitation type is a foundational concept in meteorology (Ahrens 2009;Lutgens and Tarbuck 2013) as has been confirmed by previous studies in Iran (Rahimzadeh et al 2009). Because of the natural complexity in precipitation formation, changes in precipitation are expected to begin later than changes in temperature.…”

Section: Resultsmentioning

confidence: 58%

Detection of trends in days with extreme temperatures in Iran from 1961 to 2010

Araghi

Baygi

Adamowski

2015

Theor Appl Climatol

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Human health and comfort, crop productivity, water resource availability, as well as other critical hydrological, climatological, and ecological parameters are heavily influenced by trends in daily temperature maxima and minima (T dmax , T dmin , respectively). Using Mann-Kendall and sequential Mann-Kendall tests, trends in the number of days when T dmax ≥30°C or T dmin ≤0°C, over the period of 1961 to 2010, were examined for 30 synoptic meteorological stations in Iran. For 67 % of stations, days when T dmin ≤0°C showed a significant negative trend, while only 40 % of stations showed a significant positive trend in days when T dmax ≥ 30°C. The upward trend in T dmax became significant between 1967 and 1975, according to the station, while the downward trend in T dmin became significant between 1962 and 1974 for the same stations. Changes in precipitation type across most parts of the country show a high correlation with these temperature trends, especially with the negative trend in T dmin . This suggests that future climatological and hydrological alterations within the country, along with ensuing climatic issues (e.g., change in precipitation, drought, etc.) will require a great deal more attention.

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Section: Resultsmentioning

confidence: 58%

Detection of trends in days with extreme temperatures in Iran from 1961 to 2010

Araghi

Baygi

Adamowski

2015

Theor Appl Climatol

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“…A large number of validation studies (Rajeevan & Bhate, 2009;Xu et al, 2016;Yatagai et al, 2009;Yatagai, Xie, & Kitoh, 2005;Yatagaia & Xieb, 1998) demonstrated that APHRODITE agrees well with in situ rain gauge records. Furthermore, extensive studies have utilized APHRODITE as a reference for validation (Andermann, Bonnet, & Gloaguen, 2011;Gebregiorgis & Hossain, 2015;Kim, Kang, & Adams, 2012;Qi et al, 2016;Raziei et al, 2012;Raziei et al, 2013;Shrivastava et al, 2014) and hydrological applications (Andermann et al, 2012;Mishra et al, 2014). Details on the quality control method of the product can be found in Hamada, Arakawa, & Yatagai (2011).…”

Section: Ground Precipitation Data Set and Elevation Data Setmentioning

confidence: 99%

Systematical Evaluation of Satellite Precipitation Estimates Over Central Asia Using an Improved Error‐Component Procedure

et al. 2017

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Satellite precipitation estimates (SPEs) provide important alternative precipitation sources for various applications especially for regions where in situ observations are limited or unavailable, like central Asia. In this study, eight SPEs based on four different algorithms, namely, the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks are evaluated by using an improved evaluation system over central Asia with respect to their performance in capturing precipitation occurrence and magnitude. Both satellite‐only and gauge‐corrected versions are assessed against gauge‐gridded reference from June 2001 to May 2006. Main results show that all SPEs have difficulties in accurately estimating mountainous precipitation with great overestimation/underestimation in both winter and summer. In winter, CMORPH products fail to capture events over ice‐/snow‐covered region. In summer, large overestimations dominated by positive hit bias and missed precipitation are found for all products in northern central Asia. Interestingly, 3B42 and CMORPH products show great false alarm percentages (up to 90%) over lake region, which is more significant in summer than in winter. Significant elevation‐dependent errors exist in all products, especially for the high‐altitude regions (>3,000 m) with missed error and hit error being the two leading errors. Satellite‐only products have large systematic and random errors, while the gauge‐corrected products demonstrate significant improvements in reducing random errors. Generally, the gauge‐corrected GSMaP performs better than others with good skills in reducing various errors.

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“…Other validation studies have also been carried out by Yatagai and Xie [67] and Yatagai et al [68,69]. In addition, APHRODITE was used in extensive studies as reference dataset [60,[70][71][72][73][74] and hydrological modeling input precipitation information [75,76]. Finally, APHRODITE is the only long-term continental-scale daily product based on dense gauge observations for Asia region.…”

Section: Ground Reference Datasetmentioning

confidence: 99%

Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia

et al. 2015

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This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%).

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Atmospheric circulation types and winter daily precipitation in Iran

Cited by 40 publications

References 46 publications

Detection of trends in days with extreme temperatures in Iran from 1961 to 2010

Detection of trends in days with extreme temperatures in Iran from 1961 to 2010

Systematical Evaluation of Satellite Precipitation Estimates Over Central Asia Using an Improved Error‐Component Procedure

Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia

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