Daily Precipitation Patterns Research Articles

Comment on “Five Decades of Observed Daily Precipitation Reveal Longer and More Variable Drought Events Across Much of the Western United States”

AbstractChanges in precipitation patterns with climate change could have important impacts on human and natural systems. Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) report trends in daily precipitation patterns over the last five decades in the western United States, focusing on meteorological drought. They report that dry intervals (calculated at the annual or seasonal level) have increased across much of the southwestern U.S., with statistical assessment suggesting the results are statistically robust. However, Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) preprocess their annual (or seasonal) averages to compute 5‐year moving window averages before using established statistical techniques for trend analysis that assume independence about some fixed trend. Here we show that the moving window preprocessing violates that independence assumption and inflates the statistical significance of their trend estimates. This raises questions about the robustness of their results. We conclude by discussing the difficulty of adjusting for spatial structure when assessing time trends in a regional context.

Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients

Accurate estimates of spatial patterns of daily precipitation are critically important for numerous hydrologic applications. However, the estimation of daily precipitation is still challenging, especially in regions with sparse observation networks and complex terrain. Although satellite precipitation products can offer continuous precipitation fields on a daily scale, the data contain large bias and coarse spatial resolution, which limit the application of these products in local studies. Here, we developed a new spatially varying coefficient downscaling approach to yield a daily precipitation field with high-resolution and high-accuracy in a high mountainous watershed. First, the random effect eigenvector spatial filtering (RESF) model was introduced and improved by considering the nonlinear term; second, the residuals were corrected by applying the simple inverse distance weighted (IDW) method. Using the proposed method, named RESF-IDW, the Global Satellite Mapping of Precipitation (GSMaP) was downscaled from 0.1° to 0.01° by integrating reanalyzed atmospheric data and environmental variables as auxiliary information. The performance of RESF-IDW was evaluated and compared with the geographically weighted regression (GWR) method and the original GSMaP precipitation products at 20 national and automatic stations by using leave-one-out cross validation. The results show that compared to the original GSMaP products, RESF-IDW, RESF, GWR-IDW, and GWR exhibited improvements in their mean values of correlation coefficient (CC) by 24.5%, 17.9%, 12.0%, and 3.2%, respectively. Additionally, their mean values of mean absolute error (MAE) improved by 31.2%, 14.9%, 18.7%, and 2.4%, respectively, while their mean values of root mean square error (RMSE) improved by 24.1%, 18.5%, 9.6%, and 3.8%, respectively. The RESF-IDW model can be used to correctly improve the original GSMaP product under different precipitation intensities, with critical success index (CSI) improvements of 38.81%, 43.14% and 44.58% for low, medium and high precipitation intensities, respectively. The use of residual correction enhances the spatial accuracy (MAE decreases by 19.43% on average) and the precipitation detection capability (CSI increases by 12.09% on average) of the RESF estimations. Based on the error metrics, the RESF-IDW model can be used to generate improved precipitation estimates on a daily scale and has a good capability for precipitation detection over different intensities. The proposed method in this study provides a promising way to generate accurate daily precipitation fields with high spatial resolution over data sparse and high mountain regions.

Extensive Evaluation of Four Satellite Precipitation Products and Their Hydrologic Applications over the Yarlung Zangbo River

Satellite remote sensing precipitation products with high temporal–spatial resolution and large area coverage have great potential in hydrometeorological research. This paper analyzes the performance of four satellite products from 2000 to 2008 in the Yarlung Zangbo River Basin, namely the Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Climate Prediction Center morphing method (CMORPH). The four products are evaluated from three aspects: spatial distribution, temporal characteristics, and hydrological simulation. The results show that: (1) the four products exhibit similar annual and daily precipitation patterns, with the highest daily precipitation accuracy concentrated in the center, followed by the east and west; (2) TRMM, CHIRPS, and CMORPH exhibit the largest positive bias for monthly precipitation estimation in December, while PERSIANN shows the largest positive bias in July. All products overestimate the precipitation of 0.1–5 mm/d, and underestimate the precipitation above 5 mm/d, especially for PERSIANN; (3) certain Products tend to perform better than others at elevations of 3000–4000 m and in relatively humid zones. TRMM shows relatively stable performance for various elevation and climate zones; (4) for hydrological model validation, TRMM has the best performance during the calibration period, although it is inferior to CHIRPS during the validation period. Overall, TRMM has the highest applicability in the Yarlung Zangbo River Basin; however, its impact on the uncertainty of hydrological modeling needs to be further studied.

The use of driving endonuclease genes to suppress mosquito vectors of malaria in temporally variable environments

BackgroundThe use of gene drive systems to manipulate populations of malaria vectors is currently being investigated as a method of malaria control. One potential system uses driving endonuclease genes (DEGs) to spread genes that impose a genetic load. Previously, models have shown that the introduction of DEG-bearing mosquitoes could suppress or even extinguish vector populations in spatially-heterogeneous environments which were constant over time. In this study, a stochastic spatially-explicit model of mosquito ecology is combined with a rainfall model which enables the generation of a variety of daily precipitation patterns. The model is then used to investigate how releases of a DEG that cause a bias in population sex ratios towards males are affected by seasonal or random rainfall patterns. The parameters of the rainfall model are then fitted using data from Bamako, Mali, and Mbita, Kenya, to evaluate release strategies in similar climatic conditions.ResultsIn landscapes with abundant resources and large mosquito populations the spread of a DEG is reliable, irrespective of variability in rainfall. This study thus focuses mainly on landscapes with low density mosquito populations where the spread of a DEG may be sensitive to variation in rainfall. It is found that an introduced DEG will spread into its target population more reliably in wet conditions, yet an established DEG will have more impact in dry conditions. In strongly seasonal environments, it is thus preferable to release DEGs at the onset of a wet season to maximize their spread before the following dry season. If the variability in rainfall has a substantial random component, there is a net increase in the probability that a DEG release will lead to population extinction, due to the increased impact of a DEG which manages to establish in these conditions. For Bamako, where annual rainfall patterns are characterized by a long dry season, it is optimal to release a DEG at the start of the wet season, where the population is growing fastest. By contrast release timing is of lower importance for the less seasonal Mbita.ConclusionThis analysis suggests that DEG based methods of malaria vector control can be effective in a wide range of climates. In environments with substantial temporal variation in rainfall, careful timing of releases which accounts for the temporal variation in population density can substantially improve the probability of mosquito suppression or extinction.

Changing patterns of daily precipitation totals at the Coweeta Hydrologic Laboratory, North Carolina, USA

ABSTRACTA pattern of increasing frequency and intensity of heavy rainfall over land has been documented for several temperate regions and is associated with climate change. This study examines the changing patterns of daily precipitation at the Coweeta Hydrologic Laboratory, North Carolina, USA, since 1937 for four rain gauges across a range of elevations. We analyse seasonal total rainfall, number of rain days and the frequency of heavy rainfall. We compare these with several teleconnections, including the Bermuda High Index (BHI), the West BHI, the North Atlantic Oscillation (NAO) and the El Niño‐Southern Oscillation. Our data show a tendency for increased variability, including major periods of drought, with fewer rain days recently, especially in summer. Only autumn tended to have increases in rainfall frequency and magnitude; this is the season when orographic enhancement is at its strongest. The major driver of precipitation at Coweeta is the strength of the Bermuda High. The strength of the NAO is important in summer. The results are relevant to the southeast United States in general, given that the region comes under the influence of similar air masses during the year. The findings are applicable to the wider Appalachian Mountains and to other mountainous regions where there is significant orographic enhancement.

Analysis of spatial patterns of daily precipitation and wet spell extremes in Southeast Asia

ABSTRACTThe study investigates the spatial distribution of extreme daily rainfall and wet spells in Southeast Asia (SEA) at annual, seasonal, and intraseasonal scales using 16 years of data from the Tropical Rainfall Measurement Mission. A detailed analysis is conducted using various indices that characterize exceedance probabilities and relative contribution of extremes to the total rainfall. The selected indices include the Gini index, which is a measure of overall inequality, and is employed for the first time to examine SEA rainfall characteristics. The favoured regions (hotspots) of rainfall extremes during each season are identified, and their linkages with terrain and seasonal winds discussed. The intraseasonal variability in rainfall extremes is investigated by focusing on different phases of the Madden–Julian Oscillation (MJO). At the annual scale, four main hotspots are observed, which are located off the coast of Myanmar, over eastern Indian Ocean, northwestern Borneo and over Philippines. A majority of indices shows considerable land–sea contrast, with lower values over land. Lower Gini index as obtained for land regions indicate that the rainfall distribution, and the relative contribution of each class to the total rainfall are more uniform over land compared to sea. The land–sea contrast in indices is stronger during boreal summer and winter, and weaker during boreal fall. During boreal spring, the Myanmar coast has high probability of extremes, which further increases during boreal summer. The most evident hotspot regions during boreal fall and winter are along the coast of Vietnam, and the eastern coast of Malay Peninsula, respectively. The wind‐terrain interactions play a crucial role in determining the location of most hotspots. In general, the impact of active (suppressed) MJO phases is to increase (decrease) the probability of extremes in the study domain. However, local scale departures from this general pattern are observed, which are discussed in this work.

Analysis of periodic patterns of daily precipitation through simultaneous modeling of its serially observed occurrence and amount

Precipitation is of great importance to agriculture, environment and ecosystem as a regular precipitation pattern is usually vital to healthy plants; excessive or insufficient rainfall can be harmful. Periodic patterns of precipitation can be studied based on regularly observed data over time. Since regularly observed precipitation data are generally skewed with many zeros, two common analysis approaches have been proposed recently. One approach investigates precipitation using a two-part model where the occurrence and positive amount of precipitation are analyzed separately (Piantadosi et al. in Environ Model Assess 14:431–438, 2009), whereas the other approach handles occurrence and amount simultaneously using a Tweedie’s compound Poisson model for independent observations (Hasan and Dunn in Int J Climatol 32:1006–1017, 2012). The former approach fails to maintain the regular temporal structure of serially observed precipitation, whereas the latter approach ignores serial dependence. As there is generally substantial serial correlation in the observed sequence of precipitation data over time, we introduce a compound Poisson state-space model with serially correlated random effects for daily precipitation data. This approach characterizes both occurrence and amount of precipitation simultaneously while accounting for the corresponding serial correlation. Our main results depend only on the first- and second-moment assumptions of unobserved random effects. We illustrate our method with the analysis of the daily precipitation data recorded at Mount Washington, NH, USA.

A Markov chain probability model to describe wet and dry patterns of weather at Colombo

The hypothesis that the wet and dry patterns of daily precipitation observed in Colombo can be modeled by a first order Markov chain model was tested using daily rainfall data for a 60-year period (1941–2000). The probability of a day being wet or dry was defined with respect to the status of the previous day. Probabilities were assumed to be stationary within a given month. Except for isolated single events, the model is shown to describe the observed sequence of wet and dry spells satisfactorily depending on the season. The accuracy of modeling wet spells is high compared to dry spells. When the model-predicted mean length of wet spells for each month was compared with the estimated values from the data set, a reasonable agreement between the model prediction and estimation is seen (within ±0.1). In general, the data show a higher disagreement for the months having longer dry spells. The mean annual duration of wet spells is 2.6 days while the mean annual duration of dry spells is 3.8 days. It is shown that the model can be used to explore the return periods of long wet and dry spells. We conclude from the study that the Markov chain of order 1 is adequate to describe wet and dry patterns of weather in Colombo.

Classification of daily precipitation patterns on the basis of radar-derived precipitation rates for Saxony, Germany

Classification of daily precipitation patterns on the basis of radar-derived precipitation rates for Saxony, Germany

Predictive downscaling based on non-homogeneous hidden Markov models

Weather-state models have been shown to be effective in downscaling the synoptic atmospheric information to local daily precipitation patterns. We explore the ability of non-homogeneous hidden Markov models (NHMM) to downscale regional seasonal climate data to daily rainfall at a collection of gauging sites. The predictors used are: ensemble means of seasonal rainfall as forecast by the DEMETER and ECHAM models, and the preceding seasonal outgoing long-wave radiation (OLR). As the downscaling of seasonal GCM-based predictions lacks the ability to capture the intra-seasonal variability, we augment the seasonal GCM-driven inputs with statistically-driven predictions of the monthly rainfall amounts. The pooling effect of combining seasonal and monthly estimates of the regional rainfall enhances the capacity of the NHMM to simulate the stochastic characteristics of rainfall fields. The monthly rainfall prediction is derived from a wide range of climate precursors such as the El Niño-Southern Oscillation, local sea-level pressure, and sea-surface temperature. Application of the methodology to data from the Everglades National Park region in South Florida, USA is presented for the seasons May–July and August–September using a 22-year sequence of seasonal data from eight rainfall stations. The model skill in capturing the seasonal and intra-seasonal rainfall attributes at each station is demonstrated graphically and using simple statistical measures of efficiency. The hidden states derived from NHMM are qualitatively analysed and shown to correspond to the dominant synoptic-scale features of rainfall generating mechanisms, which reinforces the argument that physical processes are appropriately captured. Citation Khalil, A. F., Kwon, H.-H., Lall, U. & Kaheil, Y. H. (2010) Predictive downscaling based on non-homogeneous hidden Markov models. Hydrol. Sci. J. 55(3), 333–350.

Effects of simulated daily precipitation patterns on annual plant populations depend on life stage and climatic region

BackgroundTo improve the understanding of consequences of climate change for annual plant communities, I used a detailed, grid-based model that simulates the effect of daily rainfall variability on individual plants in five climatic regions on a gradient from 100 to 800 mm mean annual precipitation (MAP). The model explicitly considers moisture storage in the soil. I manipulated daily rainfall variability by changing the daily mean rain (DMR, rain volume on rainy days averaged across years for each day of the year) by ± 20%. At the same time I adjusted intervals appropriately between rainy days for keeping the mean annual volume constant. In factorial combination with changing DMR I also changed MAP by ± 20%.ResultsIncreasing MAP generally increased water availability, establishment, and peak shoot biomass. Increasing DMR increased the time that water was continuously available to plants in the upper 15 to 30 cm of the soil (longest wet period, LWP). The effect of DMR diminished with increasing humidity of the climate. An interaction between water availability and density-dependent germination increased the establishment of seedlings in the arid region, but in the more humid regions the establishment of seedlings decreased with increasing DMR. As plants matured, competition among individuals and their productivity increased, but the size of these effects decreased with the humidity of the regions. Therefore, peak shoot biomass generally increased with increasing DMR but the effect size diminished from the semiarid to the mesic Mediterranean region. Increasing DMR reduced via LWP the annual variability of biomass in the semiarid and dry Mediterranean regions.ConclusionMore rainstorms (greater DMR) increased the recharge of soil water reservoirs in more arid sites with consequences for germination, establishment, productivity, and population persistence. The order of magnitudes of DMR and MAP overlapped partially so that their combined effect is important for projections of climate change effects on annual vegetation.

Spatial analysis of precipitation intensity and concentration in Iran

Understanding rainfall variability in the arid, rugged landscapes of Iran requires studying the climatology and spatial patterns of daily precipitation. This paper applies geostatistical and other methods to daily precipitation data from 90 stations that are distributed across Iran to calculate daily rainfall concentration and intensity and map their spatial distribution. The results show that daily precipitation tends to be irregular and intense across much of Iran and that a disproportionately large share of the annual rainfall comes from a small number of high intensity to extreme rainfall events. Analysis of daily rainfall concentration confirms that extreme events are widely distributed across the country, with more than 20% of the land area exposed to the risk of extreme rainfall. The areas experiencing the most frequent extreme events are the hot, dry southern coast and the western slopes of the main north–south range, the Zagros Mountains. Even the wettest parts of the country along the Caspian Sea coast derive a significant amount of their rain from high intensity and extreme events. A cross-validation analysis supports the reliability and accuracy of the geostatistical modeling.

Classification of Daily Precipitation Patterns in South Korea using Mutivariate Statistical Methods

The cluster analysis of diurnal precipitation patterns is performed by using daily precipitation of 59 stations in South Korea from 1973 to 1996 in four seasons of each year. Four seasons are shifted forward by 15 days compared to the general ones. Number of clusters are 15 in winter, 16 in spring and autumn, and 26 in summer, respectively. One of the classes is the totally dry day in each season, indicating that precipitation is never observed at any station. This is treated separately in this study. Distribution of the days among the clusters is rather uneven with rather low area-mean precipitation occurring most frequently. These 4 (seasons)<TEX>$\times$</TEX>2 (wet and dry days) classes represent more than the half (59 %) of all days of the year. On the other hand, even the smallest seasonal clusters show at least <TEX>$5\sim9$</TEX> members in the 24 years (1973-1996) period of classification. The cluster analysis is directly performed for the major <TEX>$5\sim8$</TEX> non-correlated coefficients of the diurnal precipitation patterns obtained by factor analysis In order to consider the spatial correlation. More specifically, hierarchical clustering based on Euclidean distance and Ward's method of agglomeration is applied. The relative variance explained by the clustering is as high as average (63%) with better capability in spring (66%) and winter (69 %), but lower than average in autumn (60%) and summer (59%). Through applying weighted relative variances, i.e. dividing the squared deviations by the cluster averages, we obtain even better values, i.e 78 % in average, compared to the same index without clustering. This means that the highest variance remains in the clusters with more precipitation. Besides all statistics necessary for the validation of the final classification, 4 cluster centers are mapped for each season to illustrate the range of typical extremities, paired according to their area mean precipitation or negative pattern correlation. Possible alternatives of the performed classification and reasons for their rejection are also discussed with inclusion of a wide spectrum of recommended applications.

A Bayesian hierarchical model for local precipitation by downscaling large‐scale atmospheric circulation patterns

AbstractPrecipitation over the Western part of Iberian Peninsula is known to be related to the large‐scale sea level pressure field and thus to advection of humidity into this area. The major problem is to downscale this synoptic atmospheric information to local daily precipitation patterns. One way to handle this problem is by weather‐state models, where, based on the pressure field, each day is classified into a weather state and precipitation is then modeled within each weather state via multivariate distributions.In this paper, we propose a spatiotemporal Bayesian hierarchical model for precipitation. Basic objective and novelty of the paper is to capture and model the essential spatiotemporal relationships that exist between large‐scale sea level pressure field and local daily precipitation. A specific local spatial ordering that mimics the essential large‐scale patterns is used in the likelihood. The model is then applied to a network of rain gauge stations in the river Tagus valley. The inference is then carried out using appropriate MCMC methods. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Forecasting local daily precipitation patterns in a climate change scenario

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 28:183-197 (2005) - doi:10.3354/cr028183 Forecasting local daily precipitation patterns in a climate change scenario Jesús Abaurrea*, Jesús Asín Dpto. Métodos Estadísticos, Universidad de Zaragoza; Pedro Cerbuna 12, Zaragoza 50009, Spain *Email: abaurrea@unizar.es ABSTRACT: The present study introduces a statistical procedure for obtaining long-term local daily precipitation forecasts in a climate change scenario. It is based on a regression model that uses climate variables properly reproduced by a General Circulation Model (GCM) as predictors. The daily rainfall model used consists of a logistic regression as the occurrence model and a generalized linear model (GLM) with Gamma error distribution as the quantity model. The ability of the model to generate plausible long-term projections is analysed by studying and comparing its behaviour using observed and GCM simulated data as input. The method is applied to forecast the rainfall pattern in the area of Zaragoza (Spain) for the period 2090–2100, in an IS92a scenario. We use the data corresponding to an experiment with the CGCM1 model, the first version of the coupled GCM of the Canadian Centre for Climate Modelling and Analysis (CCCma). The results obtained show that no significant change in global rainfall frequency or in the annual accumulated amount are to be expected; however, an important modification of the seasonal cycle, with a high decrease in rainfall frequency and in the amount collected in spring, is forecasted. KEY WORDS: Statistical downscaling · Daily rainfall · Climate change Full text in pdf format NextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 28, No. 3. Online publication date: May 24, 2005 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2005 Inter-Research.

Classification of daily rainfall patterns in a Mediterranean area with extreme intensity levels: the Valencia region

AbstractThe objective of this paper is to identify the spatial distribution patterns of heavy rainfall in the Valencia region, Spain. Rainfall in this area reaches one of the highest intensity levels in the Mediterranean, with daily precipitation values above 800 mm. Although high intensities characterize the region's precipitation, there is a zone situated in the south of the Gulf of Valencia that registers exceptionally high daily precipitation intensities.Our spatial analysis is based on two different perspectives and methods. In the first, we studied the most important torrential rain events in the period 1971–95 and obtained a classification based on the geographical distribution of the daily precipitation maxima. In the second, we applied statistical techniques to classify the distribution patterns of torrential rain by using principal components analysis and cluster analysis. This analysis was conducted for the 1981–90 period. The results of both methodologies are similar and reflect the main characteristics and sub‐types of the spatial distribution of the daily precipitation patterns. These spatial patterns are directly linked to the action of topographic factors in atmospheric situations of humid easterly flows. Copyright © 2002 Royal Meteorological Society.

Surface Synoptic Circulation and Daily Precipitation in Catalonia

Catalonian daily precipitation patterns were obtained by means of S-mode Principal Component Analysis (PCA) applied to subsets built up according to synoptic surface airflow. Daily data from November to April 1990-1994 from 45 raingauge sites in Catalonia without missing data were used. Eight circulation classes for days in this period were considered. A Precipitation Activity Index (PAI) was calculated for each station and class, leading to the determination of the highest activity regions for each subset. In addition, we determined which circulation type produced most precipitation at each station of the network. Separate PCAs were performed for each of the eight classes considered and the first four Varimax rotated solutions were interpreted for each circulation type. The physical meaning of the rest of the factors were omitted as they were associated with local behaviour. Finally, some concluding remarks on the cyclogenic properties of the Western Mediterranean Basin and the diversity of the extracted patterns are presented and supported by the authors experience in forecasting in Catalonia.

A principal component and long-term trend analysis of daily precipitation in Switzerland

Daily precipitation patterns over Switzerland are investigated by rotated and unrotated principal component analysis for the periods 1901–1990 (with 113 continuously operating rain-gauge sites) and 1961–1990 (with 304 sites). Empirical orthogonal functions are utilized to homogenize the precipitation series and to optimally transform the 113 series of the long-term record into a few variables. Several statistically significant linear trends are detected. This includes, in particular, a wintertime increase in precipitation by up to 30 per cent per 100 years in the western and northern parts of Switzerland. This trend is statistically significant at the 90 per cent level. In most parts of southeastern Switzerland, winter precipitation increased as well but by a smaller rate and at a slightly lower statistical significance level. For the period 1961–1990, precipitation amounts increased in most subregions and all seasons with the exception of summer. A statistical model is formulated in order to isolate the contributions to the observed precipitation trends that are related to changes in either the frequency or the precipitation activity of Alpine weather classes. It is demonstrated that the observed precipitation trends cannot be interpreted based upon changing frequencies of weather classes (the observed changes would in fact lead to drier wintertime conditions), but are rather dominated by the tendency of most rain-producing weather types to produce more rain. Some physical mechanisms that could account for this unexpected result are also discussed. © 1997 by the Royal Meteorological Society. Int. J. Climatol., 17: 1333–1356 (1997) (No. of Figs: 9. No. of Tables: 5. No. of

Daily Precipitation Patterns over Wales: Towards a Detailed Precipitation Climatology

Standardized daily precipitation patterns over Wales for more than 1000 days on which significant falls occurred are grouped using cluster analysis to yield nine major pattern groups. Comparison of the pattern groups with the Lamb circulation, near-surface airflows and significant synoptic situations yields a detailed synoptic precipitation climatology which clearly relates daily precipitation pattern to a limited number of specific synoptic situations and their associated airflows. The limitations inherent within the Lamb classification when applied to a small area also emerge. Near-surface airflow and, in particular, the more detailed surface synopsis are more reliable predictors of daily precipitation patterns.