scholarly journals Understanding the Predictability within Convection-Allowing Ensemble Forecasts in East China: Meteorological Sensitivity, Forecast Error Growth and Associated Precipitation Uncertainties Across Spatial Scales

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 234 ◽  
Author(s):  
Xiaoran Zhuang ◽  
Naigeng Wu ◽  
Jinzhong Min ◽  
Yuan Xu
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Forecast Error ◽  
Strong Relationship ◽  
Ensemble Forecast ◽  
Observation System ◽  
Dabie Mountains ◽  
Moist Convection ◽  
Error Growth ◽  
Error Energy

This study investigates the practical predictability of two simulated mesoscale convective systems (MCS1 and MCS2) within a state-of-the-art convection-allowing ensemble forecast system. The two MCSs are both controlled by the synoptic Meiyu-front but differ in mesoscale orographic forcing. An observation system simulation experiment (OSSE) setup is first built, which includes flow-dependent multiple-scale initial and lateral boundary perturbations and a 12 h 30-member ensemble forecast is thereby created. In combination with the difference total energy, the decorrelation scale and the ensemble sensitivity analysis, both forecast error evolution, precipitation uncertainties and meteorological sensitivity that describe the practical predictability are assessed. The results show large variabilities of precipitation forecasts among ensemble members, indicative of the practical predictability limit. The study of forecast error evolution shows that the error energy in the MCS1 region in which the convection is blocked by the Dabie Mountains exhibits a simultaneous peak pattern for all spatial scales at around 6 h due to strong moist convection. On the other hand, when large-scale flow plays a more important role, the forecast error energy in the MCS2 region exhibits a stepwise increase with increasing spatial scale. As a result of error energy growth, the precipitation uncertainties evolve from small scales and gradually transfer to larger scales, implying a strong relationship between error growth and precipitation across spatial scales, thus explaining the great precipitation variability within ensemble members. These results suggest the additional forcing brought by the Dabie Mountains could regulate the predictability of Meiyu-frontal convection, which calls for a targeted perturbation design in convection-allowing ensemble forecast systems with respect to different forcing mechanisms.

scholarly journals Predictability in Wet and Dry Convective Turbulence

2008 ◽  
Vol 65 (1) ◽  
pp. 220-234 ◽  
Author(s):  
K. Spyksma ◽  
P. Bartello
Keyword(s):  
Convective Motion ◽  
Atmospheric Model ◽  
Moist Convection ◽  
Error Growth ◽  
Heating And Cooling ◽  
Extra Energy ◽  
Moisture Effects ◽  
Large Ensembles ◽  
Error Energy ◽  
Dry Convection

Abstract There is a growing interest in understanding the role that moisture plays in atmospheric dynamics, particularly in its effect on predictability. Current research indicates that when moisture effects are added to an atmospheric model, the error growth produced by the new moist dynamics reduces the predictability times, especially at the scales of moist convection. The issue of moist convection’s effect on predictability is addressed herein. By performing high-resolution large-ensemble runs, it is shown that although nonprecipitating moist convection is less predictable than dry convection resulting from the same forcing, this effect can be explained by the energy injected into the system through the latent heating and cooling arising from the convective motion. This extra energy is spread evenly over most scales of the convective dynamics. When the predictability times are scaled to account for the extra kinetic energy, and the resulting earlier growth of error energy, wet and dry convection have very similar error growth characteristics. Sensitivity tests are performed to ensure that the results from the large ensembles have converged and that they are consistent with either changing resolution, diffusion levels, initial error energy length scales, or forcing amplitude.

Error Growth Dynamics within Convection-Allowing Ensemble Forecasts over Central U.S. Regions for Days of Active Convection

2021 ◽  
Author(s):  
Xiaoran Zhuang ◽  
Ming Xue ◽  
Jinzhong Min ◽  
Zhiming Kang ◽  
Naigeng Wu ◽  
...  
Keyword(s):  
Diurnal Cycle ◽  
Large Scale ◽  
Response Times ◽  
Forecast Error ◽  
Small Scale ◽  
Forecast Errors ◽  
Error Growth ◽  
Ensemble Forecasts ◽  
Scale Error ◽  
Active Convection

AbstractError growth is investigated based on convection-allowing ensemble forecasts starting from 0000 UTC for 14 active convection events over central to eastern U.S. regions from spring 2018. The analysis domain is divided into the NW, NE, SE and SW quadrants (subregions). Total difference energy and its decompositions are used to measure and analyze error growth at and across scales. Special attention is paid to the dominant types of convection with respect to their forcing mechanisms in the four subregions and the associated difference in precipitation diurnal cycles. The discussions on the average behaviors of error growth in each region are supplemented by 4 representative cases. Results show that the meso-γ-scale error growth is directly linked to precipitation diurnal cycle while meso-α-scale error growth has strong link to large scale forcing. Upscale error growth is evident in all regions/cases but up-amplitude growth within own scale plays different roles in different regions/cases.When large-scale flow is important (as in the NE region), precipitation is strongly modulated by the large-scale forcing and becomes more organized with time, and upscale transfer of forecast error is stronger. On the other hand, when local instability plays more dominant roles (as in the SE region), precipitation is overall least organized and has the weakest diurnal variations. Its associated errors at the γ– and β-scale can reach their peaks sooner and meso-α-scale error tends to rely more on growth of error with its own scale. Small-scale forecast errors are directly impacted by convective activities and have short response time to convection while increasingly larger scale errors have longer response times and delayed phase within the diurnal cycle.

scholarly journals Study on Scale-Selective Initial Perturbation for Regional Ensemble Forecast

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 285
Author(s):  
Yu Xia ◽  
Hanbin Zhang ◽  
Jing Chen
Keyword(s):  
Large Scale ◽  
Forecast Error ◽  
Initial Perturbation ◽  
Ensemble Forecast ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Comparison Results ◽  
Sensitivity Tests ◽  
Ensemble Transform Kalman Filter ◽  
Selection Of

To improve the skills of the regional ensemble forecast system (REFS), a modified ensemble transform Kalman filter (ETKF) initial perturbation strategy was developed. First, sensitivity tests were conducted to investigate the influence of the perturbation scale on the ensemble spread growth and forecast skill. In addition, the scale characteristic of the forecast error was analyzed based on the results of these tests, and a new initial condition perturbation method was developed through scale-selection of the ETKF perturbations, namely, ETKF-SS (scale-selective ETKF). The performances of the ETKF-SS scheme and the original ETKF (hereinafter referred to as ETKF) scheme were tested and compared. The results showed that the large-scale perturbations were much easier to grow than the original ETKF perturbations. In addition, scale analysis of the forecast error showed that the large-scale errors showed significant growth at the upper levels, while the small and meso-scale errors grew fast at the lower levels. The comparison results of the ETKF-SS and the ETKF showed that the ETKF-SS perturbations had more obvious growth than the ETKF perturbations, and the ETKF-SS perturbations in the short-term forecast lead times were more precise than the ETKF perturbations. The ensemble forecast verification results showed that the ETKF-SS ensemble had a larger spread and smaller root mean square error than the ETKF at short forecast lead times, while the probabilistic scores of the ETKF-SS also outperformed those of the ETKF method. In addition, the ETKF-SS ensemble can provide a better precipitation forecast than the ETKF.

Dynamics and Structure of Mesoscale Error Covariance of a Winter Cyclone Estimated through Short-Range Ensemble Forecasts

2005 ◽  
Vol 133 (10) ◽  
pp. 2876-2893 ◽  
Author(s):  
Fuqing Zhang
Keyword(s):  
Short Range ◽  
Large Scale ◽  
Small Scale ◽  
Horizontal Distance ◽  
Moist Convection ◽  
Error Growth ◽  
State Variables ◽  
Ensemble Forecasts ◽  
Error Covariance ◽  
Cross Covariance

Abstract Several sets of short-range mesoscale ensemble forecasts generated with different types of initial perturbations are used in this study to investigate the dynamics and structure of mesoscale error covariance in an intensive extratropical cyclogenesis event that occurred on 24–25 January 2000. Consistent with past predictability studies of this event, it is demonstrated that the characteristics and structure of the error growth are determined by the underlying balanced dynamics and the attendant moist convection. The initially uncorrelated errors can grow from small-scale, largely unbalanced perturbations to large-scale, quasi-balanced structured disturbances within 12–24 h. Maximum error growth occurred in the vicinity of upper-level and surface zones with the strongest potential vorticity (PV) gradient over the area of active moist convection. The structure of mesoscale error covariance estimated from these short-term ensemble forecasts is subsequently flow dependent and highly anisotropic, which is also ultimately determined by the underlying governing dynamics and associated error growth. Significant spatial and cross covariance (correlation) exists between different state variables with a horizontal distance as large as 1000 km and across all vertical layers. Qualitatively similar error covariance structure is estimated from different ensemble forecasts initialized with different perturbations.

Intrinsic versus Practical Limits of Atmospheric Predictability and the Significance of the Butterfly Effect

2016 ◽  
Vol 73 (3) ◽  
pp. 1419-1438 ◽  
Author(s):  
Y. Qiang Sun ◽  
Fuqing Zhang
Keyword(s):  
Large Scale ◽  
Initial Perturbation ◽  
Forecast Accuracy ◽  
Initial Condition ◽  
Moist Convection ◽  
Error Growth ◽  
Initial Error ◽  
Baroclinic Waves ◽  
Atmospheric Predictability ◽  
Butterfly Effect

Abstract Limits of intrinsic versus practical predictability are studied through examining multiscale error growth dynamics in idealized baroclinic waves with varying degrees of convective instabilities. In the dry experiment free of moist convection, error growth is controlled primarily by baroclinic instability under which forecast accuracy is inversely proportional to the amplitude of the baroclinically unstable initial-condition error (thus the prediction can be continuously improved without limit through reducing the initial error). Under the moist environment with strong convective instability, rapid upscale growth from moist convection leads to the forecast error being increasingly less sensitive to the scale and amplitude of the initial perturbations when the initial-error amplitude is getting smaller; these diminishing returns may ultimately impose a finite-time barrier to the forecast accuracy (limit of intrinsic predictability and the so-called “butterfly effect”). However, if the initial perturbation is sufficiently large in scale and amplitude (as for most current-day operational models), the baroclinic growth of large-scale finite-amplitude initial error will control the forecast accuracy for both dry and moist baroclinic waves; forecast accuracy can be improved (thus the limit of practical predictability can be extended) through the reduction of initial-condition errors, especially those at larger scales. Regardless of the initial-perturbation scales and amplitude, the error spectrum will adjust toward the slope of the background flow. Inclusion of strong moist convection changes the mesoscale kinetic energy spectrum slope from −3 to ~−5/3. This change further highlights the importance of convection and the relevance of the butterfly effect to both the intrinsic and practical limits of atmospheric predictability, especially at meso- and convective scales.

scholarly journals Mesoscale Predictability of Moist Baroclinic Waves: Convection-Permitting Experiments and Multistage Error Growth Dynamics

2007 ◽  
Vol 64 (10) ◽  
pp. 3579-3594 ◽  
Author(s):  
Fuqing Zhang ◽  
Naifang Bei ◽  
Richard Rotunno ◽  
Chris Snyder ◽  
Craig C. Epifanio
Keyword(s):  
Large Scale ◽  
Random Noise ◽  
Baroclinic Instability ◽  
Growth Dynamics ◽  
Small Scale ◽  
Moist Convection ◽  
Error Growth ◽  
Baroclinic Waves ◽  
Convective Scale ◽  
Shear Production

Abstract A recent study examined the predictability of an idealized baroclinic wave amplifying in a conditionally unstable atmosphere through numerical simulations with parameterized moist convection. It was demonstrated that with the effect of moisture included, the error starting from small random noise is characterized by upscale growth in the short-term (0–36 h) forecast of a growing synoptic-scale disturbance. The current study seeks to explore further the mesoscale error-growth dynamics in idealized moist baroclinic waves through convection-permitting experiments with model grid increments down to 3.3 km. These experiments suggest the following three-stage error-growth model: in the initial stage, the errors grow from small-scale convective instability and then quickly [O(1 h)] saturate at the convective scales. In the second stage, the character of the errors changes from that of convective-scale unbalanced motions to one more closely related to large-scale balanced motions. That is, some of the error from convective scales is retained in the balanced motions, while the rest is radiated away in the form of gravity waves. In the final stage, the large-scale (balanced) components of the errors grow with the background baroclinic instability. Through examination of the error-energy budget, it is found that buoyancy production due mostly to moist convection is comparable to shear production (nonlinear velocity advection). It is found that turning off latent heating not only dramatically decreases buoyancy production, but also reduces shear production to less than 20% of its original amplitude.

scholarly journals An Assessment of the Performance of the Operational Global Ensemble Forecast Systems in Predicting the Forecast Uncertainty

2017 ◽  
Vol 32 (1) ◽  
pp. 149-164 ◽  
Author(s):  
Carlee F. Loeser ◽  
Michael A. Herrera ◽  
Istvan Szunyogh
Keyword(s):  
Large Scale ◽  
Forecast Error ◽  
Ensemble Forecast ◽  
Forecast Uncertainty ◽  
Ensemble Forecasts ◽  
Atmospheric Flow ◽  
Weather Forecasts ◽  
Using Data ◽  
Ensemble Systems ◽  
First Time

Abstract This study investigates the efficiency of the major operational global ensemble forecast systems of the world in capturing the spatiotemporal evolution of the forecast uncertainty. Using data from 2015, it updates the results of an earlier study based on data from 2012. It also tests, for the first time on operational ensemble data, two quantitative relationships to aid in the interpretation of the raw ensemble forecasts. One of these relationships provides a flow-dependent prediction of the reliability of the ensemble in capturing the uncertain forecast features, while the other predicts the 95th percentile value of the magnitude of the forecast error. It is found that, except for the system of the Met Office, the main characteristics of the ensemble forecast systems have changed little between 2012 and 2015. The performance of the UKMO ensemble improved in predicting the overall magnitude of the uncertainty, but its ability to predict the dominant uncertain forecast features was degraded. A common serious limitation of the ensemble systems remains that they all have major difficulties with predicting the large-scale atmospheric flow in the long (longer than 10 days) forecast range. These difficulties are due to the inability of the ensemble members to maintain large-scale waves in the forecasts, which presents a stumbling block in the way of extending the skill of numerical weather forecasts to the subseasonal range. The two tested predictive relationships were found to provide highly accurate predictions of the flow-dependent reliability of the ensemble predictions and the 95th percentile value of the magnitude of the forecast error for the operational ensemble forecast systems.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

scholarly journals The Patterns and Puzzles of Genetic Diversity of Endangered Freshwater Mussel Unio crassus Philipsson, 1788 Populations from Vistula and Neman Drainages (Eastern Central Europe)

Life ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 119
Author(s):  
Adrianna Kilikowska ◽  
Monika Mioduchowska ◽  
Anna Wysocka ◽  
Agnieszka Kaczmarczyk-Ziemba ◽  
Joanna Rychlińska ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Genetic Structure ◽  
Spatial Scales ◽  
Freshwater Mussel ◽  
Its Region ◽  
Strong Relationship ◽  
Mantel Test ◽  
Freshwater Ecosystems ◽  
Hierarchical Organization ◽  
Anthropogenic Factors

Mussels of the family Unionidae are important components of freshwater ecosystems. Alarmingly, the International Union for Conservation of Nature and Natural Resources Red List of Threatened Species identifies almost 200 unionid species as extinct, endangered, or threatened. Their decline is the result of human impact on freshwater habitats, and the decrease of host fish populations. The Thick Shelled River Mussel Unio crassus Philipsson, 1788 is one of the examples that has been reported to show a dramatic decline of populations. Hierarchical organization of riverine systems is supposed to reflect the genetic structure of populations inhabiting them. The main goal of this study was an assessment of the U. crassus genetic diversity in river ecosystems using hierarchical analysis. Different molecular markers, the nuclear ribosomal internal transcribed spacer ITS region, and mitochondrial DNA genes (cox1 and ndh1), were used to examine the distribution of U. crassus among-population genetic variation at multiple spatial scales (within rivers, among rivers within drainages, and between drainages of the Neman and Vistula rivers). We found high genetic structure between both drainages suggesting that in the case of the analyzed U. crassus populations we were dealing with at least two different genetic units. Only about 4% of the mtDNA variation was due to differences among populations within drainages. However, comparison of population differentiation within drainages for mtDNA also showed some genetic structure among populations within the Vistula drainage. Only one haplotype was shared among all Polish populations whereas the remainder were unique for each population despite the hydrological connection. Interestingly, some haplotypes were present in both drainages. In the case of U. crassus populations under study, the Mantel test revealed a relatively strong relationship between genetic and geographical distances. However, in detail, the pattern of genetic diversity seems to be much more complicated. Therefore, we suggest that the observed pattern of U. crassus genetic diversity distribution is shaped by both historical and current factors i.e. different routes of post glacial colonization and history of drainage systems, historical gene flow, and more recent habitat fragmentation due to anthropogenic factors.

scholarly journals Analysis of 220 Years of Floodplain Population Dynamics in the US at Different Spatial Scales

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 141
Author(s):  
Firoza Akhter ◽  
Maurizio Mazzoleni ◽  
Luigia Brandimarte
Keyword(s):  
Population Dynamics ◽  
Large Scale ◽  
Spatial Scales ◽  
National Level ◽  
State Level ◽  
Economic Activities ◽  
Protection Measures ◽  
Management Planning ◽  
The Us ◽  
Over Time

In this study, we explore the long-term trends of floodplain population dynamics at different spatial scales in the contiguous United States (U.S.). We exploit different types of datasets from 1790–2010—i.e., decadal spatial distribution for the population density in the US, global floodplains dataset, large-scale data of flood occurrence and damage, and structural and nonstructural flood protection measures for the US. At the national level, we found that the population initially settled down within the floodplains and then spread across its territory over time. At the state level, we observed that flood damages and national protection measures might have contributed to a learning effect, which in turn, shaped the floodplain population dynamics over time. Finally, at the county level, other socio-economic factors such as local flood insurances, economic activities, and socio-political context may predominantly influence the dynamics. Our study shows that different influencing factors affect floodplain population dynamics at different spatial scales. These facts are crucial for a reliable development and implementation of flood risk management planning.

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