Ensemble representation of uncertainty in Lagrangian satellite rainfall estimates

A new algorithm called Lagrangian Simulation (LSIM) has been developed that enables the interpolation uncertainty present in Lagrangian satellite rainfall algorithms such as the Climate Prediction Center (CPC) morphing technique (CMORPH) to be characterized using an ensemble product. The new algorithm generates ensemble sequences of rainfall fields conditioned on multiplatform multisensor microwave satellite data, demonstrating a conditional simulation approach that overcomes the problem of discontinuous uncertainty fields inherent in this type of product. Each ensemble member is consistent with the information present in the satellite data, while variation between members is indicative of uncertainty in the rainfall retrievals. LSIM is based on the combination of a Markov weather generator, conditioned on both previous and subsequent microwave measurements, and a global optimization procedure that uses simulated annealing to constrain the generated rainfall fields to display appropriate spatial structures. The new algorithm has been validated over a region of the continental United States and has been shown to provide reliable estimates of both point uncertainty distributions and wider spatiotemporal structures

Tropical Cyclones and Storm Surge Modelling Activities

The Global Disasters Alert and Coordination System (GDACS) automatically invokes ad hoc numerical models to analyse the level of the hazard of natural disasters like earthquakes, tsunamis, tropical cyclones, floods and volcanoes. The Tropical Cyclones (TCs) are among the most damaging events, due to strong winds, heavy rains and storm surge. In order to estimate the area and the population affected, all three types of the above physical impacts must be taken into account. GDACS includes all these dangerous effects, using various sources of data. The JRC set up an automatic routine that includes the TC information provided by the Joint Typhoon Warning Center (JTWC) and the National Oceanic and Atmospheric Administration (NOAA) into a single database, covering all TCs basins. This information is used in GDACS for the wind impact and as input for the JRC storm surge system. Recently the global numerical models and other TC models have notably improved their resolutions, therefore one of the first aim of this work is the assessment and implementation of new data sources for the wind, storm surge and rainfall impacts in GDACS. Moreover the TC modelling workflow has been revised in order to provide redundancy, transparency and efficiency while addressing issues of accuracy and incorporation of additional physical processes. The status of development is presented along with the outline of future steps.JRC.E.1-Disaster Risk Managemen

Evaluation of Long-Term SSM/I-Based Precipitation Records over Land

The record of global precipitation mapping using Special Sensor Microwave Imager (SSM/I) measurements now extends over two decades. Similar measurements, albeit with different retrieval algorithms, are to be used in the Global Precipitation Measurement (GPM) mission as part of a constellation to map global precipitation with a more frequent data refresh rate. Remotely sensed precipitation retrievals are prone to both magnitude (precipitation intensity) and phase (position) errors. In this study, the ground-based radar precipitation product from the Next Generation Weather Radar stage-IV (NEXRAD-IV) product is used to evaluate a new metric of error in the long-term SSM/I-based precipitation records. The new metric quantifies the proximity of two multidimensional datasets. Evaluation of the metric across the years shows marked seasonality and precipitation intensity dependence. Drifts and changes in the instrument suite are also evident. Additionally, the precipitation retrieval errors conditional on an estimate of background surface soil moisture are estimated. The dynamic soil moisture can produce temporal variability in surface emissivity, which is a source of error in retrievals. Proper filtering has been applied in the analysis to differentiate between the detection error and the retrieval error. The identification of the different types of errors and their dependence on season, intensity, instrument, and surface conditions provide guidance to the development of improved retrieval algorithms for use in GPM constellation-based precipitation data products

Improving Satellite Quantitative Precipitation Estimates By Incorporating Deep Convective Cloud Optical Depth

As Deep Convective Systems (DCSs) are responsible for most severe weather events, increased understanding of these systems along with more accurate satellite precipitation estimates will improve NWS (National Weather Service) warnings and monitoring of hazardous weather conditions. A DCS can be classified into convective core (CC) regions (heavy rain), stratiform (SR) regions (moderate-light rain), and anvil (AC) regions (no rain). These regions share similar infrared (IR) brightness temperatures (BT), which can create large errors for many existing rain detection algorithms. This study assesses the performance of the National Mosaic and Multi-sensor Quantitative Precipitation Estimation System (NMQ) Q2, and a simplified version of the GOES-R Rainfall Rate algorithm (also known as the Self-Calibrating Multivariate Precipitation Retrieval, or SCaMPR), over the state of Oklahoma (OK) using OK MESONET observations as ground truth. While the average annual Q2 precipitation estimates were about 35% higher than MESONET observations , there were very strong correlations between these two data sets for multiple temporal and spatial scales. Additionally, the Q2 estimated precipitation distributions over the CC, SR, and AC regions of DCSs strongly resembled the MESONET observed ones, indicating that Q2 can accurately capture the precipitation characteristics of DCSs although it has a wet bias . SCaMPR retrievals were typically three to four times higher than the collocated MESONET observations, with relatively weak correlations during a year of comparisons in 2012. Overestimates from SCaMPR retrievals that produced a high false alarm rate were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated MESONET stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the SCaMPR false alarm rate of retrieved precipitation especially over non-precipitating (anvil) regions of a DCS. Preliminary testing of this new algorithm to identify precipitating area has produced significant improvements over the current SCaMPR algorithm. This modified version of SCaMPR can be used to provide precipitation estimates in gaps of radar and rain gauge coverage to aid in hydrological and flood forecasting

Investigating Aerosol Effects on Clouds, Precipitation and Regional Climate in US and China by Means of Ground-based and Satellite Observations and a Global Climate Model

Aerosols affect climate by scattering/absorbing radiation and by acting as cloud condensation nuclei (CCN) or ice nuclei (IN). One of the least understood but most significant aspects of climate change is the aerosol effect on cloud and precipitation. A hypothesis has recently been proposed that, in addition to reducing cloud effective radius and suppressing precipitation, aerosols may also modify the thermodynamic structure of deep convective clouds and lead to enhanced precipitation when complex thermodynamic processes are involved. Taking advantage of the long-term and extensive ground-based observations at the US Department of Energy's Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, we thoroughly tested such a hypothesis and provide direct evidence of it. Moreover, the hypothesis is also supported by analysis of satellite-based observations over tropical regions from multiple sensors in the A-Train satellites constellation. Extensive analyses of the long-term ground-based and large-scale data reveal significant increases in rain rate or frequency and cloud top heights with increasing aerosol loading for mix-phase deep convective clouds, decreases rain frequency for low liquid clouds, but little impact on cloud height for liquid clouds. Rigorous tests are conducted to investigate any potential artifacts and influences of meteorological conditions. Large-scale circulation patterns and monsoon systems can be changed by scattering and absorption of solar radiation by aerosols. By means of model simulations with the National Center for Atmospheric Research Community Climate Model (NCAR/CCM3), we found that the increase of aerosol loading in China contributes to circulation changes, leading to more frequent occurrence of fog events in winter as observed from meteorological records. The increase in atmospheric aerosols over China heats the atmosphere and generates a cyclonic circulation anomaly over eastern-central China. This circulation anomaly leads to a reduction in the influx of dry and cold air over that area during winter. Weakening of the East Asian winter monsoon system may also contribute to these changes. All these changes favor the formation and maintenance of fog over this region. The MODerate resolution Imaging Spectroradiometer (MODIS) aerosol products used in the above studies are validated using ground-based measurements from the Chinese Sun Hazemeter Network (CSHNET). Overall, substantial improvement was found in the current version of aerosol products relative to the previous one. At individual sites, the improvement varies with surface and atmospheric conditions

Advances in Hurricane Research

This book provides a wealth of new information, ideas and analysis on some of the key unknowns in hurricane research. Topics covered include the numerical prediction systems for tropical cyclone development, the use of remote sensing methods for tropical cyclone development, a parametric surface wind model for tropical cyclones, a micrometeorological analysis of the wind as a hurricane passes over Houston, USA, the meteorological passage of numerous tropical cyclones as they pass over the South China Sea, simulation modelling of evacuations by motorised vehicles in Alabama, the influence of high stream-flow events on nutrient flows in the post hurricane period, a reviews of the medical needs, both physical and psychological of children in a post hurricane scenario and finally the impact of two hurricanes on Ireland. Hurricanes discussed in the various chapters include Katrina, Ike, Isidore, Humberto, Debbie and Charley and many others in the North Atlantic as well as numerous tropical cyclones in the South China Sea

Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward)

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 119 (2014): 5244–5269, doi:10.1002/2013JC009648.A high-resolution global daily analysis of ocean surface vector winds (1987 onward) was developed by the Objectively Analyzed air-sea Fluxes (OAFlux) project. This study addressed the issues related to the development of the time series through objective synthesis of 12 satellite sensors (two scatterometers and 10 passive microwave radiometers) using a least-variance linear statistical estimation. The issues include the rationale that supports the multisensor synthesis, the methodology and strategy that were developed, the challenges that were encountered, and the comparison of the synthesized daily mean fields with reference to scatterometers and atmospheric reanalyses. The synthesis was established on the bases that the low and moderate winds (<15 m s−1) constitute 98% of global daily wind fields, and they are the range of winds that are retrieved with best quality and consistency by both scatterometers and radiometers. Yet, challenges are presented in situations of synoptic weather systems due mainly to three factors: (i) the lack of radiometer retrievals in rain conditions, (ii) the inability to fill in the data voids caused by eliminating rain-flagged QuikSCAT wind vector cells, and (iii) the persistent differences between QuikSCAT and ASCAT high winds. The study showed that the daily mean surface winds can be confidently constructed from merging scatterometers with radiometers over the global oceans, except for the regions influenced by synoptic weather storms. The uncertainties in present scatterometer and radiometer observations under high winds and rain conditions lead to uncertainties in the synthesized synoptic structures.The project is sponsored by the NASA Ocean Vector Wind Science Team (OVWST) activities under grant NNA10AO86G.2015-02-1

Ensemble-characterisation of satellite rainfall uncertainty and its impacts on the hydrological modelling of a sparsely gauged basin in Western Africa

Many areas of the planet lack the infrastructure required to make accurate and timely estimations of rainfall. This problem is especially acute in sub-Saharan Africa, where a paucity of rain recording radar and sufficiently dense raingauge networks combine with highly variable rainfall, a reliance on agriculture that is predominantly rain fed and systems that are prone to flooding and drought. Satellite Rainfall Estimates (SRFE) are useful as they can provide additional spatial and temporal information to drive various downstream environmental models and early warning systems (EWS). However, when operating at higher spatial and temporal resolutions SRFE contain large uncertainties which propagate through the downstream models.This thesis uses the TAMSAT1 SRFE algorithm developed by Teo (2006) to estimate the rainfall over a large, data sparse and heterogenous catchment in the Senegal Basin. The uncertainty within the TAMSAT1 SRFE is represented using a set of ensemble estimates, each unique but equiprobable based on the full conditional distribution of the recorded rainfall, produced using the TAMSIM algorithm, also developed by Teo (2006). The ensemble rainfall estimates were then used in turn to drive a Pitman Rainfall-Runoff model of the catchment hydrology.The use of ensemble rainfall estimates was shown to be incompatible with the pre-calibrated parameter values for the hydrological model. A novel approach, the EnsAll method, was developed to calibrate the hydrological model which incorporated each individual ensemble member. The EnsAll calibrated model showed the greatest skill when driven by the ensemble rainfall estimates and little bias. A comparison of the hydrographs produced from TAMSIM ensemble rainfall estimates and that from an ensemble of perturbed TAMSAT1 estimates showed that the full spatio-temporally distributed method used by TAMSIM is superior to a simpler perturbation method for characterizing SRFE uncertainty.Overall, the SRFE used were shown to outperform the rainfall estimates produced from the sparse raingauge network as a hydrological model driver. However, they did demonstrate a lack of ability to represent the large interseasonal variations in rainfall resulting in large systematic biases. These biases were observed propagating directly to the modelled hydrological ouput