Mean Daily Data Research Articles

Comparison of synthetic unit hydrograph methods for flood assessment in a dryland, poorly gauged basin (Napostá Grande, Argentina)

<p>This paper examined and compared the reliability of two popular unit hydrograph methods for flood assessment in a dryland, poorly gauged basin: the Soil Conservation Service (NRCS-UH) method and the geomorphologic instantaneous unit hydrograph (GIUH) model. In addition, two different estimates of the basin's time of concentration were compared, along with varying values of the runoff curve number, to compute the watershed lag. Simulations were performed for the upper Napostá Grande (SW Buenos Aires, Argentina), using eight historic rainfall-runoff events to validate the resulting hydrograph at the basin outlet. Validation used runoff volume, peak flow, and recession time as an alternative to time to peak, for which only mean daily data were available. Results revealed great discrepancies in unit hydrograph parameters for varying determination methods, time of concentration estimates, and basin lag factors, as well as lower-than-standard peak rate factors for GIUH hydrographs. The comparison of simulated with observed hydrographs suggested a better agreement of GIUH for the highest retardance factor, as it produced the smaller peaks with the longer recession. This study informs on the complex relationships involved in unit hydrograph (UH) determination for the studied basin and warns about the variability of obtained results depending on the applied methodology, the caution needed in the systematic use of standard parameters, and the importance of verifying the accuracy of results. This provides a valuable framework for flood assessment within regional, ungauged basins with similar characteristics, which may exhibit comparable total runoff volumes for the same rainfall event but not necessarily equivalent flood hydrographs.</p>

Properties of the mixing layer height retrieved from ceilometer measurements in Slovakia and its relationship to the air pollutant concentrations.

Mixing layer height (MLH) is an important meteorological parameter for air quality since it significantly affects ground-level pollution in the atmosphere. This study examined the properties of the MLH on diurnal and seasonal timescales over a 3-year period (2020-2022) using high temporal resolution measurements from eight Vaisala CL31 ceilometers situated around Slovakia. Hourly averaged MLH data was retrieved from the BL-View software using merged method. The highest daily maxima for the MLH occurred mostly in summer and spring, while the lowest values occurred predominantly during winter and autumn. The average MLH daily maximum in summer was 2229m, and just 859m in winter. During summer, the spatial distribution of the MLH daily maxima was more uniform compared to winter, when the air masses within the individual valleys did not mix well. Correlations between ground-level pollutant concentrations and hourly mean/daily mean MLH were analyzed. The highest correlation, R≈0.6, was found for O3. For PM10, PM2.5, and NOx, the anticorrelations with MLH were found with maximum in winter (R ≈ - 0.3 for hourly data and R ≈ - 0.5 for daily mean data) but no relation in summer. Lastly, the ceilometer MLH was compared to the radiosonde retrieved MLH for various cloud covers. Our analysis is based on an extensive set of empirical data, which can improve the accuracy and effectiveness of meteorological and atmospheric chemistry models. The findings can support air pollution forecasting and warning systems, providing valuable insights for policymakers and researchers.

Events of the Cross-Equatorial Flows over the Maritime Continent during 1979–2020

Abstract This study proposed a definition of the cross-equatorial flow (CEF) events over the Maritime Continent (MC) based on the daily mean data derived from ERA5 from 1979 to 2020. Following this definition, a total of 138 CEF events were identified. The statistical analysis suggests that CEF events are concentrated from May to October, with the peak frequency appearing in July. The occurrence frequency generally decreases along with the increase in duration, with a total of 66% of events persisting no longer than the average (7.1 days), while approximately 16% persist exceeding 10 days. The intensity of events is generally the strongest in August, consistent with the climatological annual cycle of CEF. The CEF events are characterized by a seesaw pattern of equatorial meridional wind anomalies between the upper and lower troposphere, with the strongest southerly (northerly) anomalies located at 925 (150) hPa. Both the upper- and lower-level wind anomalies propagate westward along with the evolution of events. The CEF events are highly connected with the tropical dipole convection, featured by the enhanced convection over the western North Pacific (WNP) and suppressed convection over MC. The convection enhancement over the WNP can be traced back to the equatorial central Pacific, which explains the westward movement of the events-related equatorial meridional wind anomalies. By contrast, the suppressed convection over the Indian Ocean and MC keeps stagnant. The events-related convection anomalies act as a branch of boreal summer intraseasonal oscillations (BSISOs), but CEF events are not fully dependent on BSISOs.

Historic Spatial Patterns of Storm-Driven Compound Events in UK Estuaries

AbstractCompound estuarine flooding is driven by extreme sea-levels and river discharge occurring concurrently, or in close succession, and threatens low-lying coastal regions worldwide. We hypothesise that these drivers of flooding rarely occur independently and co-operate at sub-daily timescales. This research aimed to identify regions and individual estuaries within Britain susceptible to storm-driven compound events, using 27 tide gauges linked to 126 river gauges covering a 30-year record. Five methods were evaluated, based on daily mean, daily maximum, and instantaneous 15-min discharge data to identify extremes in the river records, with corresponding skew surges identified within a ‘storm window’ based on average hydrograph duration. The durations, relative timings, and overlap of these extreme events were also calculated. Dependence between extreme skew surge and river discharge in Britain displayed a clear east–west split, with gauges on the west coast showing stronger correlations up to 0.33. Interpreting dependence based on correlation alone can be misleading and should be considered alongside number of historic extreme events. The analyses identified 46 gauges, notably the Rivers Lune and Orchy, where there has been the greatest chance and most occurrences of river-sea extremes coinciding, and where these events readily overlapped one another. Our results were sensitive to the analysis method used. Most notably, daily mean discharge underestimated peaks in the record and did not accurately capture likelihood of compound events in 68% of estuaries. This has implications for future flood risk in Britain, whereby studies should capture sub-daily timescale and concurrent sea-fluvial climatology to support long-term flood management plans.

Spatiotemporal Analysis of Variability in Domestic PM10 Data Using Grid Based Spatial Interpolation Method

This study analyzed spatiotemporal variability in domestic PM10 data from 2001 to 2019. From annual numbers of stations between 175 and 484, the point data at each station were spatially interpolated using the inverse distance weighted method. A periodic variability in daily mean data was examined through wavelet analysis, which showed that there was a clear annual pattern with the periodic change following a regular cycle. The Mann-Kendall Test for monthly and annual mean data showed a decreasing trend in about 1 µg/m<sup>3</sup> per year. The spatial change in the grid data for annual mean data represented that it was relatively higher in the northern regions than that in the southern regions and its mean and deviation decreased significantly over time. For the entire period of observation data, it was found that annual mean and standard deviation of PM10 concentrations were relatively high in the region near the metropolitan area.

Methods to estimate evapotranspiration in humid and subtropical climate conditions

Selecting appropriate reference evapotranspiration (ET o ) methods is crucial for managing water resources efficiently. Statistical criteria commonly used to assess the performance of empirical ET o models on a station level may produce inconsistent results, making ranking approaches a complex process. This study was conducted in India's Humid and Subtropical region, considering 11 years of mean daily data from 2009 to 2019. We evaluated thirty empirical ET o models, which were categorized into four groups based on the input parameters, namely, temperature-based (10), radiation-based (10), mass transfer-based (9), and combination model (1). The results show that the observed ET o reached maximum magnitude during Monsoon followed by Spring, Summer, Autumn, Winter, and pre-Winter season; the average observed ET o from 2009 to 2019 was ≈ 1163 mm. Among temperature-based and radiation-based models, the Hargreaves model with RMSE of 1.45 mm/day and the Turc model with RMSE of 1.01 mm/day yielded the best ET o predictions under the humid, sub-tropical climate conditions. The radiation-based models demonstrate higher accuracy in the prediction of ET o than the temperature-based and mass transfer-based models. The FAO56-PM technique, Turc model, Hargreaves model, Makkink model, and Papadakis model were ranked as the five best models among all 30 tested models. Overall, the FAO56-PM method outperformed among all 30 selected models. Thus, the exact calculation of ET o is essential for many agricultural water engineering applications, particularly in developing countries with a lack of meteorological data records and limited resources to conduct long-term in-situ observation of evapotranspiration. The methodological approach proposed in this work applies to any other location for a simple yet rigorous evaluation of evapotranspiration empirically. • Tested ET 0 models demonstrate substantial differences among them. • Mass transfer-based models had the worst performances among the tested ET 0 models. • Radiation-based and temperature-based models demonstrated the best performance. • FAO Penman-Monteith and Turc models demonstrated the best performance.

Temporal variation of wind speed and wind direction and interrelationship between air temperature and wind speed for different climatic seasons Northern State, Sudan

The research aims to study the temporal variability of wind speed and direction and their correlation with air temperature in different climatic seasons in Northern State, to produce broad data base on winds variability and interrelationship between air temperature and wind speed for different climatic seasons. Mean monthly and daily climatic data dating back to 2001(first season) and 2011(second season) were obtained from Karima meteorological station representing three different climatic seasons namely: April for summer, August for autumn and December for winter. Data were subjected to simple regression analysis, statistical analysis and computations were done by “Excel 2000”. The results showed that the wind speed varied widely during each month and day even within the same climatic season. In two seasons, the diurnal variation for different climatic seasons Apr., Aug. and Dec. was much lower than the monthly; due to the higher monthly variability of wind speed. The results also revealed that the prevailing direction of winds was generally northwards. The results of April in the first season are presented as an example; the mean daily wind speed (Va) ranged from 0.6 to 17m/s, with a mean of 7.2m/s, a STD of 4.3 and a CV of 60%. The daily mean data ranged from 5.4 (3hrs) to 8.2 (24hrs), with a mean of 7.2m/s, a STD of 1.1 and a CV of 15.3%.The prevailing wind direction decreases in the following order: N>NE=NNE>NNW. The monthly variability of the air temperature in the two seasons decreased in the following order: winter> summer> autumn. Significant, inversely proportional and cubic wind speed-temperature correlations in the two seasons for Aug. and Dec. were (P<0.05, r=0.362); (P<0.01, r= 0.488); (P<0.001, R=585) and (P<0.001, r= 0.679), respectively.

GÜNLÜK ÇÖZÜNMÜŞ OKSİJEN KONSANTRASYONUNUN ÇOK DEĞİŞKENLİ UYARLANABİLİR REGRESYON EĞRİLERİ İLE TAHMİN EDİLMESİ

Bu çalışmada su sıcaklığı (T), özgül iletkenlik (Öİ) verilerinden hesaplanmış elektriksel iletkenlik (Eİ), pH ve debi (Q) verileri kullanılarak çok değişkenli uyarlanabilir regresyon eğrileri (MARS) ve regresyon analizi (RA) yöntemleri ile ÇO konsnatrasyonunun tahmin edilmesi amaçlanmıştır. MARS yönteminde en iyi tahmin değerlerini üreten temel fonksiyonlar ve denklemler belirlenmiş, RA yöntemi doğrusal, üs, üstel ve kuadratik olmak üzere dört farklı fonksiyona uygulanmış ve bu fonksiyonlara ait katsayılar hesaplanmıştır. Modelleme çalışmalarında Amerika Birleşik Devletleri’nin Oregon eyaletinin kuzey batısında yer alan Willamette Nehri’nin yan kollarından biri olan ve yaklaşık 2435 km2’lik bir havza alanına sahip Clackamas Nehri’ne ait Eylül 2016 − Ağustos 2017 dönemi günlük ortalama verileri kullanılmıştır. Her bir su kalitesi değişkeninin ÇO konsantrasyonu tahmin performansına etkisini belirlemek amacıyla sekiz farklı model oluşturulmuştur. ÇO konsantrasyonu tahmininde kurulan modellerin ve kullanılan yöntemlerin performanslarının değerlendirilebilmesi için çeşitli istatistikler (ortalama karesel hatanın karekökü, ortalama mutlak hata, saçılım indeksi ve Nash Sutcliffe verimlilik katsayısı) kullanılmıştır. Modelleme çalışmalarından elde edilen sonuçlar irdelendiğinde, MARS yönteminin RA yönteminden daha iyi sonuçlar verdiği anlaşılmıştır. Regresyon fonksiyonları içerisinden ise en başarılı tahmin sonuçlarının kuadratik fonksiyondan elde edildikleri ve MARS yöntemi ile elde edilen değerlere de oldukça yakın oldukları görülmüştür. ÇO konsantrasyonu tahmininde en etkili değişkenlerin T ve Q oldukları dolayısıyla en etkisiz değişkenlerin ise Eİ ve pH oldukları anlaşılmıştır. Model 3, Model 5, Model 7 ve Model 8’den elde edilen sonuçların birbirine çok yakın olması sebebiyle daha az değişken ile güçlü tahminler yapması ve daha sade bir model olması bakımından ÇO tahmininde Model 3’ün kullanılmasının daha avantajlı olacağı sonucuna varılmıştır.

Hey Google! will New Zealand vote to legalise cannabis? Using Google Trends data to predict the outcome of the 2020 New Zealand cannabis referendum

New Zealand held a referendum on the legalisation of recreational cannabis in October 2020. Polls preceding the referendum provided contrasting outcomes. We investigated whether internet search data from Google Trends could provide an alternative estimate of the referendum outcome. We assessed various methods for accessing Google Trends data, downloading search probability data for google.com searches from New Zealand via trends.google.com, PyTrends and Google Trends Extended for Health. We used daily data for the three months prior to the final referendum date, and hourly data for the final week. We defined two smaller time frames each from daily and hourly data, allowing comparisons over the entire time frames, and progressively closer to the end. Using the selected keyword combination of 'cannabis referendum yes/no' we calculated the proportions of 'yes' and 'no' searches for each time frame/data source combination, aiming for a prediction within 2% of the final result. Data from different sources varied slightly. The method used to aggregate search probabilities over the selected time frame (mean/median) resulted in changes in the predicted outcome for hourly-, but not daily data. On 20 October we predicted the 'no' vote at 51.9%-55.4% for daily-, and 60% for hourly data when aggregated using the median, but only 49% for mean hourly data. Hourly data performed poorly at predicting the final 51.2% 'no' result, while predictions based on mean daily data for the full voting period provided the best prediction, differing by 0.1-0.2%. Predictions based on Google Trends data broadly agreed with polling predictions, but the exact method used affected the eventual prediction. While polls are subject to influence from methodological considerations (e.g., sampling), it is clear that Google Trends data can be used to make a prediction, but do not present a magic bullet solution to polling problems.

Dietary intake of energy, nutrients and phenolic compounds in Italian primary school children and their environmental impact

AbstractData from observational and interventional studies demonstrate the key role of diet on both human health and environmental sustainability. However, there is a lack of studies investigating the environmental impact of children's dietary behavior. In this regard, detailed information on population-level intakes is required to translate these findings into possible recommendations.The aim of this study was to investigate the nutritional composition of the diet and its related environmental impact in a sample of Italian primary school children (n = 200, 8–11 years, 52% F - 48% M).Children completed a 3-day food record twice, in winter and spring. Dietary records were processed to obtain mean daily data on food group intakes. Mean daily energy and nutrient intakes were computed applying the Italian food database of the European Institute of Oncology, while (poly)phenolic compound contents were estimated from the Phenol-Explorer or by specific searches in the most up to date scientific literature. The mean daily environmental impact was estimated through the Barilla Center for Food and Nutrition environmental database taking into account the carbon footprint and ecological footprint. Children's BMI was calculated by measuring height and weight at the beginning of each assessment week.No differences in children's BMI as well as in the consumption of food groups were found between the two seasons. The intra-individual variability was low but a high inter-individual variability was registered. Energy and nutrient intakes were similar between the two seasons and were found to be in line with national data referred to school age children. Adherence to the Italian nutritional recommendations (LARN) was not satisfied for total fats, sugars, and some vitamins and minerals. The main contributors to the phenolic intake were flavonoids and phenolic acids, while the most representative subclasses were flavan-3-ols and hydroxycinnamic acids. Main dietary sources of (poly)phenolic compounds were fruit juices, apples, pears, potatoes, and tomatoes. Carbon and ecological footprints were higher during winter and the main dietary contributors were red meat; milk, yogurt and dairies; pizza and focaccia, for both indexes.This study provided the first comprehensive analysis of the nutritional composition and the environmental impact of Italian primary school children diet. Further research is needed to evaluate children habitual diet for investigating a possible relationship between health and environmental sustainability. Moreover, school education programs have to be implemented for promoting healthy and sustainable dietary behaviors.

Impact of using multiple-satellite sensors on the accuracy of daily-mean sea surface wind data

ABSTRACT Most satellites observing sea surface winds have sun-synchronous orbits and provide observation data at the same place two times per a day. A daily mean value estimated from these data suffers from sampling errors because the high-frequency variation including a diurnal change in the wind field cannot be neglected. To overcome this problem, the use of multiple satellites is useful. The purposes of this study were to describe the time variation of the accuracy of daily mean wind data in a third-generation Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO3) and to investigate its causes by comparison with in situ measurement data from moored buoys. The results reveal that the three statistical measures such as bias, Root Mean Square error and cross correlation coefficient have improved over time. A set of scatter diagrams of the number of satellites versus the statistical measures for each year shows strong correlations. The accuracy of daily mean data provided by J-OFURO3 is concluded to depend on the number of satellites. We also focused on specific time intervals for satellite wind observations, particularly the maximum missing time interval (MMTI) within a day. The results showed that the correlations between the three statistical measures and MMTI were quite high. Because the above-mentioned two causes are not independent, we analysed the combined effect of the two causes together. The results show that the accuracy of daily mean data depends more strongly on MMTI than on the number of observations. By applying these results, it is possible to determine the optimal number of sensors and the optimal observation time to achieve maximum target accuracy. This provides very useful information for the design of satellite observation systems for sea surface wind.

Impact of summer heat on mortality and years of life lost: Application of a novel indicator of daily excess hourly heat

Previous studies have widely assessed heat-mortality relationships across global regions, while the epidemiological evidence regarding the heat effect on years of life lost (YLL) is relatively sparse. Current investigations using daily mean data cannot take hourly temperature variation into consideration and may underestimate heat effects. We developed a novel indicator, daily excess hourly heat (DEHH), to precisely evaluate the potential heat effects on mortality and YLL. Hourly data on temperature and daily information, including concentrations of air pollutants, relative humidity, and records of all registered deaths were obtained in Wuhan, China during the warm seasons (May-September) of 2009-2012. DEHH, developed in this study, is defined as daily total hourly temperatures that exceed a specific heat threshold. By performing time series regression analyses, we assessed the changes in daily mortality and YLL per interquartile range (IQR) increase in DEHH across different lag days. The heat threshold evaluated by the Akaike Information Criterion for DEHH calculation is 30 °C (92th percentile of whole-year mean temperature distribution). Daily average DEHH was 13.9 °C, with an IQR of 19.9 °C. Linear exposure-response curves were found between DEHH and two health outcomes. Generally, heat effects lasted for 2-3 days and DEHH at lag 0-1 was most strongly associated with increased mortality and YLL. The effects were especially remarkable for stroke and ischemic heart disease mortality. Most intense effect on YLL was found in non-accidental deaths (20.11, 95% confidence interval: 8.90-31.33) at lag 0-1. More DEHH-related mortality and YLL from cardiovascular deaths were observed among males. People aged 0-74 years and males suffered more from YLL burden due to high temperatures. Our study demonstrated that DEHH may be an alternative indicator to precisely measure heat effects on daily mortality and YLL. Further DEHH-based evidence from large scale investigations is needed so as to better understand heat-associated health burden and improve public response to extremely high temperatures.

Comparison of the partitioning of evapotranspiration – numerical modeling with different isotopic models using various kinetic fractionation coefficients

In the context of a warming climate and dry conditions, aggravating water shortages, research on partitioning total evapotranspiration (ET) into soil evaporation (E) and plant transpiration (T) is needed. Recently, using the oxygen isotope ratio as a tracer has proved to be a valuable way to better partition ET. In this study, we carefully considered the process of heavy water fractionation during the transpiration process, and specifically, we modified the kinetic fractionation coefficient (αk2) of transpiration, based on previous formulations used to estimate it. Our results show that, for the hourly and daily mean data set, both the isotopic–steady–state (ISS) and non–steady–state (NSS) assumptions for δ18O of leaf water (δL,b) provided a good fit with observed δL,b when using the modified αk2. In contrast, using αk2 values traditionally assigned led to significant deviations from observed δL,b (p < 0.05), potentially influencing ET partitioning results. On diurnal time scales, the percent contribution of T to total ET (FT) is sensitive to different model assumptions and different formulations to estimate αk2. The modeled FT, assuming NSS conditions and using the modified αk2 value, led to the best agreement with observed values. In contrast, on longer time scales (days), using the ISS assumption to partition ET is adequate, as the NSS assumption could introduce more complexities and uncertainties. Our study demonstrates that the stable isotope technique is a promising utility for quantitatively partitioning ET. To more accurately estimate FT, we also call on a better description of the nature of αk2 of transpiration.

Interannual Variations of the First Rainy Season Precipitation over South China

The first rainy season (FRS), also known as the presummer rainy season, is the first standing stage of the East Asian summer monsoon when over 40% of the annual precipitation is received over South China. Based on the start and end dates of the FRS defined by the China Meteorological Administration, this study investigates the interannual variations of the FRS precipitation over South China and its mechanism with daily mean data. The length and start/end date of the FRS vary year to year, and the average length of the FRS is 90 days, spanning from 6 April to 4 July. Composite analyses reveal that the years with abundant FRS precipitation over South China feature weakened anticyclonic wind shear over the Indochina Peninsula in the upper troposphere, southwestward shift of the western Pacific subtropical high, and anticyclonic wind anomalies over the South China Sea in the lower troposphere. The lower-tropospheric southwesterly wind anomalies are especially important because they help to enhance warm advection and water vapor transport toward South China, increase the lower tropospheric convective instability, and shape the pattern of the anomalous ascent over South China. It is further proposed that a local positive feedback between circulation and precipitation exists in this process. The variability of the FRS precipitation can be well explained by a zonal sea surface temperature (SST) dipole in the tropical Pacific and the associated Matsuno–Gill-type Rossby wave response over the western North Pacific. The interannual variability of both the SST dipole and the FRS precipitation over South China is weakened after the year 2000.

Variational assimilation of mean daily observation data for the problem of sea hydrothermodynamics

Abstract The mathematical model of hydrothermodynamics of the Baltic Sea is considered with the pole moved to a neighbourhood of St. Petersburg in order to improve the horizontal resolution in the Gulf of Finland. The problem of variational assimilation of mean daily data for sea surface temperature (SST) is formulated and studied for the given type of calculation grid of this model. A new algorithm of solution of the inverse problem for reconstruction of the heat flux on the interface of two media is proposed on the base of variational assimilation of satellite observation data. Results of numerical experiments are presented for reconstruction of heat fluxes in the problem of variational assimilation of mean daily observations of SST data.

An investigation of temporal variability of the total column ozone over tropical urban, high altitude, and coastal stations in Western Maharashtra, India

ABSTRACTDistribution and variability of ozone are vital to the atmospheric thermal structure as it can exert great influence on climate. In this study, the Microtops II Ozonometer (Microtops)-measured total column ozone (TCO) data archived at the tropical urban, high altitude, and coastal observing sites during 2012–2015 are analysed to investigate the temporal structure of ozone. Results reveal that the TCO exhibits a non-negligible diurnal variability depicting distinct seasonal behaviour, which corroborates well with the Indian as well as the worldwide measurements of TCO. The mean rate of ozone diurnal change (Vs) in winter is found to be maximum (approximately 2.1 DU h–1) while it is minimum (about 0.53 DU h–1) in pre-monsoon. In spite of the prevalent variability of the order of about 2–9 DU amongst Microtops channels and Ozone Monitoring Instrument on board the NASA EOS/AURA spacecraft (OMI-AURA) measurements, there exists a strong monthly/seasonal variation in both the ground- and satellite-based TCO measurements. Monthly mean OMI-AURA TCO variation presents a nearly perfect sinusoidal wave with a coefficient of determination (R2) equal to 0.76. Monthly TCO is maximum in May/June and minimum in December/January. The noticeable diurnal and monthly TCO variability could be due to a complex combination of photochemical processes in the lower troposphere and the transport in the middle and upper troposphere. Linear regression technique applied to the Microtops and OMI-AURA data sets show that the two data sets are better correlated with a correlation coefficient (r) taking values 0.71, 0.77, and 0.61 for channels I, II, and III, respectively. The three Microtops channels show the dispersion of about 8–11 DU around 1:1 regression line which is of the order of one standard deviation of the daily mean data set. The TCO data at all Microtops channels either underestimate or overestimate with respect to the OMI-AURA measurements since the values for slopes of the linear regression line for all the three channels are ≤1. Pearson’s product moment correlation analysis indicates that the TCO anti-correlates with ultraviolet-B (UV-B) irradiance (vis-à-vis through UV index) as the Pearson’s product moment correlation coefficients are found to be in the range –0.52 to –0.97.

Quantifying the Eddy–Jet Feedback Strength of the Annular Mode in an Idealized GCM and Reanalysis Data

Abstract A linear response function (LRF) that relates the temporal tendency of zonal-mean temperature and zonal wind to their anomalies and external forcing is used to accurately quantify the strength of the eddy–jet feedback associated with the annular mode in an idealized GCM. Following a simple feedback model, the results confirm the presence of a positive eddy–jet feedback in the annular mode dynamics, with a feedback strength of 0.137 day−1 in the idealized GCM. Statistical methods proposed by earlier studies to quantify the feedback strength are evaluated against results from the LRF. It is argued that the mean-state-independent eddy forcing reduces the accuracy of these statistical methods because of the quasi-oscillatory nature of the eddy forcing. Assuming the mean-state-independent eddy forcing is sufficiently weak at the low-frequency limit, a new method is proposed to approximate the feedback strength as the regression coefficient of low-pass-filtered eddy forcing onto the low-pass-filtered annular mode index. When time scales longer than 200 days are used for the low-pass filtering, the new method produces accurate results in the idealized GCM compared to the value calculated from the LRF. The estimated feedback strength in the southern annular mode converges to 0.121 day−1 in reanalysis data using the new method. This work also highlights the significant contribution of medium-scale waves, which have periods less than 2 days, to the annular mode dynamics. Such waves are filtered out if eddy forcing is calculated from daily mean data. The present study provides a framework to quantify the eddy–jet feedback strength in GCMs and reanalysis data.

ASoP (v1.0): a set of methods for analyzing scales of precipitation in general circulation models

Abstract. General circulation models (GCMs) have been criticized for their failure to represent the observed scales of precipitation, particularly in the tropics where simulated daily rainfall is too light, too frequent and too persistent. Previous assessments have focused on temporally or spatially averaged precipitation, such as daily means or regional averages. These evaluations offer little actionable information for model developers, because the interactions between the resolved dynamics and parameterized physics that produce precipitation occur at the native gridscale and time step. We introduce a set of diagnostics (Analyzing Scales of Precipitation, version 1.0 – ASoP1) to compare the spatial and temporal scales of precipitation across GCMs and observations, which can be applied to data ranging from the gridscale and time step to regional and sub-monthly averages. ASoP1 measures the spectrum of precipitation intensity, temporal variability as a function of intensity and spatial and temporal coherence. When applied to time step, gridscale tropical precipitation from 10 GCMs, the diagnostics reveal that, far from the dreary persistent light rainfall implied by daily mean data, most models produce a broad range of time step intensities that span 1–100 mm day−1. Models show widely varying spatial and temporal scales of time step precipitation. Several GCMs show concerning quasi-random behavior that may influence and/or alter the spectrum of atmospheric waves. Averaging precipitation to a common spatial ( ≈ 600 km) or temporal (3 h) resolution substantially reduces variability among models, demonstrating that averaging hides a wealth of information about intrinsic model behavior. When compared against satellite-derived analyses at these scales, all models produce features that are too large and too persistent.

Determining new threshold temperatures for cooling and heating degree day index of different climatic zones of Iran

Abstract Iran is a country with a variety of different climates. Determining the threshold temperatures suitable for providing thermal and climatic comfort is necessary and vital to its population well-being. This research presents new threshold temperatures in order to calculate the degree day index required for heating and cooling by taking advantage of the 12 stations that are representative of the diversity of Iran's climate. Using Olgyay diagram, different bioclimatic ranges of 12 weather stations and their frequencies were compiled, processes and analysed. Mean daily data of temperature and relative humidity were used for the period of 1950–2010. Based on the frequencies of temperature readings falling in Olgyay's diagram comfort zone, representive temperature thresholds were selected based on 40 to 60 percentiles or (P20), 25–75% percentile (P50) and the threshold of 10–90% percentile. The findings of this study shows that Mashhad with 29.6% and Anzali with 2.33% of frequencies, have experienced the maximum and minimum days of comfort. After analyzing various percentiles to determine the threshold temperatures, it was observed that there is a little difference among the stations for determining the minimum threshold for the comfort. Differences are more obvious in the maximum thresholds. In total, minimum base temperatures (HDD) belonged to Ardabil stations that were 20.50, 20.90 and 20 deg C for P20, P50 and P80 respectively. The maximum temperature for calculating CDD with values of (P20 = 25 °C; P50 = 26.25 °C; P80 = 27.50 °C) is dedicated to Zabol station. The findings present more reasonable thermal comfort thresholds that can be used by architects, engineers and policy makers to achieve, in turn, more energy efficient homes and high quality indoor and outdoor living environments.

One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models

Abstract The intra-day time-varying pattern of solar data is more informative than the aggregated mean daily data. However, most of the traditional forecasting models often construct the 1-day ahead daily power forecast based on its historical daily averages but ignore the information from its intra-day dynamic pattern. Intuitively, the use of aggregated data could cause certain loss of information in forecasting, which in turn adversely affects forecasting accuracy. In order to make use of the valuable trajectory information of the power output within a day, this paper suggests a partial functional linear regression model (PFLRM) for forecasting the daily power output of PV systems. The PFLRM is a generalization of the traditional multiple linear regression model but enables to model nonlinearity structure. Compared to the neural network models that are often criticized by the requirements of past experience and reliable knowledge in the design of network architecture, the suggested method only involves a few parameter estimates. A regularized algorithm was used to estimate the PFLRM parameters. It is shown that the regularized PFLRM improves the forecast accuracy of power output over the traditional multiple linear regression and neural network models. The results were validated based on a 2.1 kW grid connected PV system.