Statistical Properties of Global Precipitation in the NCEP GFS Model and TMPA Observations for Data Assimilation

Abstract Assimilation of satellite precipitation data into numerical models presents several difficulties, with two of the most important being the non-Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, improving the model forecast beyond a few hours by assimilating precipitation has been found to be difficult. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low-resolution NCEP Global Forecast System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA). The samples are constructed using the same model with the same forecast period, observation variables, and resolution as in the follow-on GFS/TMPA precipitation assimilation experiments presented in the companion paper. The statistical results indicate that the T62 and T126 GFS models generally have positive bias in precipitation compared to the TMPA observations, and that the simulation of the marine stratocumulus precipitation is not realistic in the T62 GFS model. It is necessary to apply to precipitation either the commonly used logarithm transformation or the newly proposed Gaussian transformation to obtain a better relationship between the model and observational precipitation. When the Gaussian transformations are separately applied to the model and observational precipitation, they serve as a bias correction that corrects the amplitude-dependent biases. In addition, using a spatially and/or temporally averaged precipitation variable, such as the 6-h accumulated precipitation, should be advantageous for precipitation assimilation.

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GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM_IMERGDF Product and ASTER DEM in EDBF Algorithm

Remote Sensing ◽

10.3390/rs12193162 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3162 ◽

Cited By ~ 1

Author(s):

Sana Ullah ◽

Zhengkang Zuo ◽

Feizhou Zhang ◽

Jianghua Zheng ◽

Shifeng Huang ◽

...

Keyword(s):

Spatial Resolution ◽

Thermal Emission ◽

Empirical Distribution ◽

Mainland China ◽

Annual Precipitation ◽

Coefficient Of Determination ◽

Precipitation Variable ◽

Precipitation Analysis ◽

Global Precipitation Mission ◽

Global Precipitation

To obtain the high-resolution multitemporal precipitation using spatial downscaling technique on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. In this research, a new downscaling methodology such as the global precipitation mission (GPM)-based multitemporal weighted precipitation analysis (GMWPA) at 0.05° resolution is developed and applied in the humid region of Mainland China by employing the GPM dataset at 0.1° and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in empirical distribution-based framework (EDBF) algorithm. The proposed methodology is a two-stepped process in which a scale-dependent regression analysis between each individual precipitation variable and the EDBF-based weighted precipitation with geospatial predictor(s), and to downscale the predicted multitemporal weighted precipitation at a refined scale is developed for the downscaling of GMWPA. While comparing results, it shows that the weighted precipitation outperformed all precipitation variables in terms of the coefficient of determination (R2) value, whereas they outperformed the annual precipitation variables and underperformed as compared to the seasonal and the monthly variables in terms of the calculated root mean square error (RMSE) value. Based on the achieved results, the weighted precipitation at the low-resolution (e.g., at 0.75° resolution) along-with the original resolution (e.g., at 0.1° resolution) is employed in the downscaling process to predict the average multitemporal precipitation, the annual total precipitation for the year 2001 and 2004, and the average annual precipitation (2001–2015) at 0.05° resolution, respectively. The downscaling approach resulting through proposed methodology captured the spatial patterns with greater accuracy at higher spatial resolution. This work showed that it is feasible to increase the spatial resolution of a precipitation variable(s) with greater accuracy on an annual basis or as an average from the multitemporal precipitation dataset using a geospatial predictor as the proxy of precipitation through the weighted precipitation in EDBF environment.

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Fine-scale features on the sea surface in SAR satellite imagery – Part 1: Simultaneous in-situ measurements

Ocean Science Discussions ◽

10.5194/osd-9-2885-2012 ◽

2012 ◽

Vol 9 (5) ◽

pp. 2885-2914 ◽

Cited By ~ 3

Author(s):

A. Soloviev ◽

C. Maingot ◽

S. Matt ◽

R. E. Dodge ◽

S. Lehner ◽

...

Keyword(s):

Numerical Models ◽

Three Dimensional ◽

Companion Paper ◽

In Situ Measurements ◽

Ocean Dynamics ◽

Sea Surface ◽

Upper Ocean ◽

Fine Scale ◽

Sar Imagery

Abstract. This work is aimed at identifying the origin of fine-scale features on the sea surface in synthetic aperture radar (SAR) imagery with the help of in-situ measurements as well as numerical models (presented in a companion paper). We are interested in natural and artificial features starting from the horizontal scale of the upper ocean mixed layer, around 30–50 m. These features are often associated with three-dimensional upper ocean dynamics. We have conducted a number of studies involving in-situ observations in the Straits of Florida during SAR satellite overpass. The data include examples of sharp frontal interfaces, wakes of surface ships, internal wave signatures, as well as slicks of artificial and natural origin. Atmospheric processes, such as squall lines and rain cells, produced prominent signatures on the sea surface. This data has allowed us to test an approach for distinguishing between natural and artificial features and atmospheric influences in SAR images that is based on a co-polarized phase difference filter.

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The role of Edge-Driven Convection in the generation of volcanism I: a 2D systematic study

10.5194/se-2020-120 ◽

2020 ◽

Author(s):

Antonio Manjón-Cabeza Córdoba ◽

Maxim Ballmer

Keyword(s):

Numerical Models ◽

High Volume ◽

Oceanic Lithosphere ◽

Companion Paper ◽

Small Scale ◽

Continental Lithosphere ◽

Transient Phenomenon ◽

Mantle Viscosity ◽

Wide Range

Abstract. The origin of intraplate volcanism is not explained by the plate tectonic theory, and several models have been put forward for explanation. One of these models involves Edge-Driven Convection (EDC), in which cold and thick continental lithosphere is juxtaposed to warm and thin oceanic lithosphere to trigger convective instability. To test whether EDC can produce long-lived high-volume magmatism, we run numerical models of EDC for a wide range of mantle properties and edge (i.e., the oceanic-continental transition) geometries. We find that the most important parameters that govern EDC are the rheological paramaters mantle viscosity η0 and activation energy Ea. However, even the maximum melting volumes found in our models are insufficient to account for island-building volcanism on old seafloor, such as at the Canary Islands and Cape Verde. Also, beneath old seafloor, localized EDC-related melting commonly transitions into widespread melting due to small-scale sublithospheric convection, inconsistent with the distribution of volcanism at these volcanic chains. In turn, EDC is a good candidate to sustain the formation of small seamounts on young seafloor, as it is a highly transient phenomenon that occurs in all our models soon after initiation. In a companion paper, we investigate the implications of interaction of EDC with mantle-plume activity.

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The role of edge-driven convection in the generation of volcanism – Part 1: A 2D systematic study

Solid Earth ◽

10.5194/se-12-613-2021 ◽

2021 ◽

Vol 12 (3) ◽

pp. 613-632

Author(s):

Antonio Manjón-Cabeza Córdoba ◽

Maxim D. Ballmer

Keyword(s):

Numerical Models ◽

High Volume ◽

Oceanic Lithosphere ◽

Companion Paper ◽

Rheological Parameters ◽

Small Scale ◽

Transient Phenomenon ◽

Mantle Viscosity ◽

Wide Range

Abstract. The origin of intraplate volcanism is not explained by plate tectonic theory, and several models have been put forward for explanation. One of these models involves edge-driven convection (EDC), in which cold and thick continental lithosphere is juxtaposed with warm and thin oceanic lithosphere to trigger convective instability. To test whether EDC can produce long-lived high-volume magmatism, we run numerical models of EDC for a wide range of mantle properties and edge (i.e., the oceanic–continental transition) geometries. We find that the most important parameters that govern EDC are the rheological parameters mantle viscosity η0 and activation energy Ea. However, even the maximum melting volumes predicted by our most extreme cases are insufficient to account for island-building volcanism on old seafloor, such as at the Canary Islands and Cabo Verde. Also, beneath old seafloor, localized EDC-related melting commonly transitions into widespread melting due to small-scale sublithospheric convection, inconsistent with the distribution of volcanism at these volcano chains. In turn, EDC is a good candidate to sustain the formation of small seamounts on young seafloor, as it is a highly transient phenomenon that occurs in all our models soon after initiation. In a companion paper, we investigate the implications of interaction of EDC with mantle plume activity (Manjón-Cabeza Córdoba and Ballmer, 2021).

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3. Numerical models of non-Gaussian processes and fields

Numerical Modelling of Random Processes and Fields ◽

10.1515/9783110941999-004 ◽

1996 ◽

pp. 59-95

Keyword(s):

Gaussian Processes ◽

Numerical Models ◽

Non Gaussian

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Impacts of the Horizontal and Vertical Grids on the Numerical Solutions of the Dynamical Equations. Part I: Nonhydrostatic Inertia-Gravity Modes

10.5194/gmd-2017-234 ◽

2017 ◽

Author(s):

Celal S Konor ◽

David A. Randall

Keyword(s):

Normal Mode ◽

Numerical Solutions ◽

Numerical Models ◽

Normal Mode Analysis ◽

Companion Paper ◽

Wave Number ◽

Mode Analysis ◽

Dynamical Equations ◽

Anelastic Equations ◽

Vertical Grids

Abstract. We have used a normal-mode analysis to investigate the impacts of the horizontal and vertical discretizations on the numerical solutions of the nonhydrostatic anelastic inertia-gravity modes on a midlatitude f-plane. The dispersion equations are derived from the linearized anelastic equations that are discretized on the Z, C, D, CD, (DC), A, E, and B horizontal grids, and on the L and CP vertical grids. The effects of both horizontal grid spacing and vertical wave number are analyzed, and the role of nonhydrostatic effects is discussed. We also compare the results of the normal-mode analyses with numerical solutions obtained by running linearized numerical models based on the various horizontal grids. The sources and behaviors of the computational modes in the numerical simulations are also examined. Our normal-mode analyses with the Z, C, D, A, E and B grids generally confirm the conclusions of previous shallow-water studies for the cyclone resolving scales (with low horizontal wavenumbers). We conclude that for cloud-resolving resolutions (with high horizontal wavenumbers) the Z and C grids become overall more accurate than for the cyclone-resolving scales, aided by nonhydrostatic effects. A companion paper, Part II, discusses the impacts of the discretization on the Rossby modes on a midlatitude β-plane.

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Analysis of a damaged 12-storey frame-wall concrete building during the 2010 Haiti earthquake — Part II: Nonlinear numerical simulations

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2012-0099 ◽

2013 ◽

Vol 40 (8) ◽

pp. 803-814 ◽

Cited By ~ 5

Author(s):

Benoit Boulanger ◽

Patrick Paultre ◽

Charles-Philippe Lamarche

Keyword(s):

Structural Damage ◽

Numerical Models ◽

Dynamic Properties ◽

Shear Walls ◽

Element Model ◽

Companion Paper ◽

Ambient Vibration ◽

Wall Structure ◽

Haiti Earthquake ◽

Concrete Building

After the 2010 Haiti earthquake, which destroyed a significant part of the seismically vulnerable city of Port-au-Prince, the country’s capital, a 12-storey reinforced concrete building that behaved well was investigated to understand its dynamic response. This paper completes the experimental work presented in a companion paper, in which the dynamic properties of the building were obtained from ambient vibration tests, and from which a finite-element model was updated. This paper’s main objectives are: (i) to understand the causes that led to the observed structural damage; and (ii) to estimate the likely seismic excitation at the site of the building. Several nonlinear analyses involving various ground motion intensities were conducted and the results were compared with the damage reported during the on-site survey. The numerical models reproduced the observed damages well and helped to explain them. The overall response of the mixed stiff frame–wall structure was clearly dominated by the high stiffness of the shear walls, showing that this type of structural system helps in keeping reasonable interstorey drift levels. Overall, the building’s structure seems to have responded linearly to all the ground motions investigated, but deformation demands imposed to the frame by the shear walls lead to local damages.

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Reduced non-Gaussianity by 30-second rapid update inconvective-scale numerical weather prediction

10.5194/npg-2021-15 ◽

2021 ◽

Author(s):

Juan Ruiz ◽

Guo-Yuan Lien ◽

Keiichi Kondo ◽

Shigenori Otsuka ◽

Takemasa Miyoshi

Keyword(s):

Kalman Filter ◽

Numerical Weather Prediction ◽

Forecast Error ◽

Weather Prediction ◽

Numerical Weather ◽

Convective Clouds ◽

Environment Model ◽

Error Distributions ◽

Ensemble Transform Kalman Filter ◽

Non Gaussian

Abstract. Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of non-Gaussianity of forecast error distributions at 1-km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments are performed with the SCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (Local Ensemble Transform Kalman Filter) assimilating every-30-second phased array radar observations. The results show that non-Gaussianity develops rapidly within convective clouds and is sensitive to the DA frequency and the number of assimilated observations. The non-Gaussianity is reduced by up to 40 % when the assimilation window is shortened from 5 minutes to 30 seconds, particularly for vertical velocity and radar reflectivity.

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Volatility Modelling of the BRICS Stock Markets

Statistics Optimization & Information Computing ◽

10.19139/soic-2310-5070-977 ◽

2020 ◽

Vol 8 (3) ◽

pp. 749-772

Author(s):

Rosinah M Mukhodobwane ◽

Caston Sigauke ◽

Wilbert Chagwiza ◽

Winston Garira

Keyword(s):

South African ◽

Portfolio Management ◽

Stock Markets ◽

Chinese Market ◽

Student’S T Distribution ◽

Error Distributions ◽

Student’S T ◽

Non Gaussian ◽

Student's T Distribution ◽

T Distribution

Volatility modelling is a key factor in equity markets for risk and portfolio management. This paper focuses on the use of a univariate generalized autoregressive conditional heteroscedasticity (GARCH) models for modelling volatility of the BRICS (Brazil, Russia, India, China and South Africa) stock markets. The study was conducted under the assumptions of seven error distributions that include the normal, skewed-normal, Student’s t, skewed-Student’s t, generalized error distribution (GED), skewed-GED and the generalized hyperbolic (GHYP) distribution. It was observed that using an ARMA(1, 1)-GARCH(1, 1) model, volatilities of the Brazilian Bovespa and the Russian IMOEX markets can both be well characterized (or described) by a heavy-tailed Student’s t distribution, while the Indian NIFTY market’s volatility is best characterized by the generalized hyperbolic (GHYP) distribution. Also, the Chinese SHCOMP and South African JALSH markets’ volatilities are best described by the skew-GED and skew-Student’s t distribution, respectively. The study further observed that the persistence of volatility in the BRICS markets does not follow the same hierarchical pattern under the error distributions, except under the skew-Student’s t and GHYP distributions where the pattern is the same. Under these two assumptions, i.e. the skew-Student’s t and GHYP, in a descending hierarchical order of magnitudes, volatility with persistence is highest in the Chinese market, followed by the South African market, then the Russian, Indian and Brazilian markets, respectively. However, under each of the five non-Gaussian error distributions, the Chinese market is the most volatile, while the least volatile is the Brazilian market.

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RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale

10.5194/egusphere-egu21-1762 ◽

2021 ◽

Author(s):

Christian Schroeder de Witt ◽

Catherine Tong ◽

Valentina Zantedeschi ◽

Daniele De Martini ◽

Alfredo Kalaitzis ◽

...

Keyword(s):

Climate Change ◽

Machine Learning ◽

Satellite Imagery ◽

Numerical Models ◽

Modeling Framework ◽

Precipitation Forecasting ◽

Ground Station ◽

Extreme Precipitation Events ◽

Station Data ◽

Global Precipitation

Climate change is expected to aggravate extreme precipitation events, directly impacting the livelihood of millions. Without a global precipitation forecasting system in place, many regions &#8211; especially those constrained in resources to collect expensive ground station data &#8211; are left behind. To mitigate such unequal reach of climate change, a solution is to alleviate the reliance on numerical models (and by extension ground station data) by enabling machine-learning-based global forecasts from satellite imagery. Though prior works exist in regional precipitation nowcasting, there lacks work in global, medium-term precipitation forecasting. Importantly, a common, accessible baseline for meaningful comparison is absent. In this work, we present RainBench, a multi-modal benchmark dataset dedicated to advancing global precipitation forecasting. We establish baseline tasks and release PyRain, a data-handling pipeline to enable efficient processing of decades-worth of data by any modeling framework. Whilst our work serves as a basis for a new chapter on global precipitation forecasting from satellite imagery, the greater promise lies in the community joining forces to use our released datasets and tools in developing machine learning approaches to tackle this important challenge.

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