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Thomas C. van Leth, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet

Abstract

We investigate the spatiotemporal structure of rainfall at spatial scales from 7 m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatiotemporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

Open access
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

Abstract

This paper describes a new Passive Microwave Empirical Cold Surface Classification Algorithm (PESCA) developed for snow-cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, since several studies have highlighted the influence of snow-cover radiative properties on the falling-snow passive microwave signature. The developed method is based on the exploitation of the lower-frequency channels (<90 GHz), common to most microwave radiometers. The method applied to the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the cross-track-scanning Advanced Technology Microwave Sounder (ATMS) is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust both for sensors in dry conditions (total precipitable water < 10 mm) and for mean surface elevation < 2500 m, independent of the cloud cover. The algorithm shows very good performance for cold temperatures (2-m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA (overall detection statistics: probability of detection is 0.98 for ATMS and 0.92 for GMI, false alarm ratio is 0.01 for ATMS and 0.08 for GMI, and Heidke skill score is 0.72 for ATMS and 0.69 for GMI). Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometries, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes also appear to be different at high frequency (>90 GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically scanning and cross-track-scanning radiometers, including the future operational EUMETSAT Polar System Second Generation (EPS-SG) mission microwave radiometers.

Open access
Alberto Ortolani, Francesca Caparrini, Samantha Melani, Luca Baldini, and Filippo Giannetti

Abstract

Measuring rainfall is complex, due to the high temporal and spatial variability of precipitation, especially in a changing climate, but it is of great importance for all the scientific and operational disciplines dealing with rainfall effects on the environment, human activities, and economy. Microwave (MW) telecommunication links carry information on rainfall rates along their path, through signal attenuation caused by raindrops, and can become measurements of opportunity, offering inexpensive chances to augment information without deploying additional infrastructures, at the cost of some smart processing. Processing satellite telecom signals brings some specific complexities related to the effects of rainfall boundaries, melting layer, and nonweather attenuations, but with the potential to provide worldwide precipitation data with high temporal and spatial samplings. These measurements have to be processed according to the probabilistic nature of the information they carry. An ensemble Kalman filter (EnKF)-based method has been developed to dynamically retrieve rainfall fields in gridded domains, which manages such probabilistic information and exploits the high sampling rate of measurements. The paper presents the EnKF method with some representative tests from synthetic 3D experiments. Ancillary data are assumed as from worldwide-available operational meteorological satellites and models, for advection, initial and boundary conditions, and rain height. The method reproduces rainfall structures and quantities in a correct way, and also manages possible link outages. Its results are also computationally viable for operational implementation and applicable to different link observation geometries and characteristics.

Open access
Abby Stevens, Rebecca Willett, Antonios Mamalakis, Efi Foufoula-Georgiou, Alejandro Tejedor, James T. Randerson, Padhraic Smyth, and Stephen Wright

Abstract

Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation as the lead time increases, due to imperfect representation of physical processes and incomplete knowledge of initial conditions. Similarly, statistical methods drawing upon established climate teleconnections have low prediction skill due to the complex nature of the climate system. Recently, promising data-driven approaches have been proposed, but they often suffer from overparameterization and overfitting due to the short observational record, and they often do not account for spatiotemporal dependencies among covariates (i.e., predictors such as sea surface temperatures). This study addresses these challenges via a predictive model based on a graph-guided regularizer that simultaneously promotes similarity of predictive weights for highly correlated covariates and enforces sparsity in the covariate domain. This approach both decreases the effective dimensionality of the problem and identifies the most predictive features without specifying them a priori. We use large ensemble simulations from a climate model to construct this regularizer, reducing the structural uncertainty in the estimation. We apply the learned model to predict winter precipitation in the southwestern United States using sea surface temperatures over the entire Pacific basin, and demonstrate its superiority compared to other regularization approaches and statistical models informed by known teleconnections. Our results highlight the potential to combine optimally the space–time structure of predictor variables learned from climate models with new graph-based regularizers to improve seasonal prediction.

Open access
Clément Guilloteau, Antonios Mamalakis, Lawrence Vulis, Phong V. V. Le, Tryphon T. Georgiou, and Efi Foufoula-Georgiou

Abstract

Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatiotemporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable trade-off between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple nonparametric implementation of sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution. To improve the interpretability of the results, especially when several modes of similar amplitude exist within the same frequency band, we propose a rotation of the complex-valued eigenvectors to optimize their spatial regularity (smoothness). The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to global historical geopotential height (GPH) and sea surface temperature (SST) daily time series, the method accurately captures patterns of atmospheric Rossby waves at high frequencies (3–60-day periods) in both GPH and SST and El Niño–Southern Oscillation (ENSO) at low frequencies (2–7-yr periodicity) in SST. At high frequencies the rsPCA successfully unmixes the identified waves, revealing spatially coherent patterns with robust propagation dynamics.

Open access
Nobuyuki Utsumi, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim

Abstract

Precipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%–50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%–50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.

Open access
Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

Abstract

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

Open access
Giuseppe Mascaro

Abstract

Intensity–duration–frequency (IDF) analyses of rainfall extremes provide critical information to mitigate, manage, and adapt to urban flooding. The accuracy and uncertainty of IDF analyses depend on the availability of historical rainfall records, which are more accessible at daily resolution and, quite often, are very sparse in developing countries. In this work, we quantify performances of different IDF models as a function of the number of available high-resolution (Nτ) and daily (N24h) rain gauges. For this aim, we apply a cross-validation framework that is based on Monte Carlo bootstrapping experiments on records of 223 high-resolution gauges in central Arizona. We test five IDF models based on (two) local, (one) regional, and (two) scaling frequency analyses of annual rainfall maxima from 30-min to 24-h durations with the generalized extreme value (GEV) distribution. All models exhibit similar performances in simulating observed quantiles associated with return periods up to 30 years. When Nτ > 10, local and regional models have the best accuracy; bias correcting the GEV shape parameter for record length is recommended to estimate quantiles for large return periods. The uncertainty of all models, evaluated via Monte Carlo experiments, is very large when Nτ ≤ 5; however, if N24h ≥ 10 additional daily gauges are available, the uncertainty is greatly reduced and accuracy is increased by applying simple scaling models, which infer estimates on subdaily rainfall statistics from information at daily scale. For all models, performances depend on the ability to capture the elevation control on their parameters. Although our work is site specific, its results provide insights to conduct future IDF analyses, especially in regions with sparse data.

Free access
Efi Foufoula-Georgiou, Clement Guilloteau, Phu Nguyen, Amir Aghakouchak, Kuo-Lin Hsu, Antonio Busalacchi, F. Joseph Turk, Christa Peters-Lidard, Taikan Oki, Qingyun Duan, Witold Krajewski, Remko Uijlenhoet, Ana Barros, Pierre Kirstetter, William Logan, Terri Hogue, Hoshin Gupta, and Vincenzo Levizzani
Full access
Clément Guilloteau and Efi Foufoula-Georgiou

Abstract

The quantitative estimation of precipitation from orbiting passive microwave imagers has been performed for more than 30 years. The development of retrieval methods consists of establishing physical or statistical relationships between the brightness temperatures (TBs) measured at frequencies between 5 and 200 GHz and precipitation. Until now, these relationships have essentially been established at the “pixel” level, associating the average precipitation rate inside a predefined area (the pixel) to the collocated multispectral radiometric measurement. This approach considers each pixel as an independent realization of a process and ignores the fact that precipitation is a dynamic variable with rich multiscale spatial and temporal organization. Here we propose to look beyond the pixel values of the TBs and show that useful information for precipitation retrieval can be derived from the variations of the observed TBs in a spatial neighborhood around the pixel of interest. We also show that considering neighboring information allows us to better handle the complex observation geometry of conical-scanning microwave imagers, involving frequency-dependent beamwidths, overlapping fields of view, and large Earth incidence angles. Using spatial convolution filters, we compute “nonlocal” radiometric parameters sensitive to spatial patterns and scale-dependent structures of the TB fields, which are the “geometric signatures” of specific precipitation structures such as convective cells. We demonstrate that using nonlocal radiometric parameters to enrich the spectral information associated to each pixel allows for reduced retrieval uncertainty (reduction of 6%–11% of the mean absolute retrieval error) in a simple k-nearest neighbors retrieval scheme.

Open access