scholarly journals Data impact studies with the AROME WMED reanalysis of the HyMeX SOP1

2020 ◽  
Author(s):  
Nadia Fourrié ◽  
Mathieu Nuret ◽  
Pierre Brousseau ◽  
Olivier Caumont
Keyword(s):  
Hydrological Cycle ◽  
Radar Data ◽  
Scale Model ◽  
Small Data ◽  
Observation Data ◽  
Short Term ◽  
Data Set ◽  
The Impact ◽  
Forecast Quality ◽  
Wind Profilers

Abstract. This paper presents the results of several observing system experiments (OSEs) performed with AROME-WMED. This model is the HyMeX (Hydrological cycle in the Mediterranean Experiment) dedicated version (Fourrié et al., 2019) of the French operational meso-scale model AROME. The second and final reanalyses assimilated most of all available data for a 2 month period corresponding to the first Special Observation Period of HyMeX. In order to assess the impact of various observation data set assimilation on the forecasts, several OSEs or also-called denial experiments, were carried out. In this study, impact of a dense reprocessed network of high quality Global Navigation Satellite System (GNSS) Zenithal Total Delay (ZTD) observations, reprocessed wind-Profilers, lidar-derived vertical profiles of humidity (ground and airborne) and Spanish radar data, is thus discussed. Among the evaluated observations, it is found that the ground-based GNSS ZTD data set provides the largest impact on the analyses and the forecasts as it represents an evenly spread and frequent data set providing information at each analysis time over the AROME-WMED domain. The impact of the reprocessing of GNSS ZTD data also improves the forecast quality but this impact is not statistically significant. The assimilation of the Spanish radar data improves the very short term forecast quality as well as the short term forecasts but this impact remains located over Spain. Marginal impact from wind profilers was observed on wind background quality. No impacts have been found regarding lidar data as they represent a very small data set.

scholarly journals Data assimilation impact studies with the AROME-WMED reanalysis of the first special observation period of the Hydrological cycle in the Mediterranean Experiment

2021 ◽  
Vol 21 (1) ◽  
pp. 463-480
Author(s):  
Nadia Fourrié ◽  
Mathieu Nuret ◽  
Pierre Brousseau ◽  
Olivier Caumont
Keyword(s):  
Hydrological Cycle ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Scale Model ◽  
Data Set ◽  
The Mediterranean ◽  
The Impact ◽  
Forecast Quality ◽  
Observation Period ◽  
Wind Profilers

Abstract. This study was performed in the framework of HyMeX (Hydrological cycle in the Mediterranean Experiment), which aimed to study the heavy precipitation that regularly affects the Mediterranean area. A reanalysis with a convective-scale model AROME-WMED (Application of Research to Operations at MEsoscale western Mediterranean) was performed, which assimilated most of the available data for a 2-month period corresponding to the first special observation period of the field campaign (Fourrié et al., 2019). Among them, observations related to the low-level humidity flow were assimilated. Such observations are important for the description of the feeding of the convective mesoscale systems with humidity (Duffourg and Ducrocq, 2011; Bresson et al., 2012; Ricard et al., 2012). Among them there were a dense reprocessed network of high-quality Global Navigation Satellite System (GNSS) zenithal total delay (ZTD) observations, reprocessed data from wind profilers, lidar-derived vertical profiles of humidity (ground and airborne) and Spanish radar data. The aim of the paper is to assess the impact of the assimilation of these four observation types on the analyses and the forecasts from the 3 h forecast range (first guess) up to the 48 h forecast range. In order to assess this impact, several observing system experiments (OSEs) or so-called denial experiments, were carried out by removing one single data set from the observation data set assimilated in the reanalysis. Among the evaluated observations, it is found that the ground-based GNSS ZTD data set provides the largest impact on the analyses and the forecasts, as it represents an evenly spread and frequent data set providing information at each analysis time over the AROME-WMED domain. The impact of the reprocessing of GNSS ZTD data also improves the forecast quality, but this impact is not statistically significant. The assimilation of the Spanish radar data improves the 3 h precipitation forecast quality as well as the short-term (30 h) precipitation forecasts, but this impact remains located over Spain. Moreover, marginal impact from wind profilers was observed on wind background quality. No impacts have been found regarding lidar data, as they represent a very small data set, mainly located over the sea.

scholarly journals The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

2015 ◽  
Vol 8 (1) ◽  
pp. 421-434 ◽  
Author(s):  
M. P. Jensen ◽  
T. Toto ◽  
D. Troyan ◽  
P. E. Ciesielski ◽  
D. Holdridge ◽  
...  
Keyword(s):  
Large Scale ◽  
Scale Model ◽  
Data Sets ◽  
Central Plains ◽  
Data Set ◽  
Convective Systems ◽  
Convective Clouds ◽  
Quality Checks ◽  
Network Operations ◽  
The Impact

Abstract. The Midlatitude Continental Convective Clouds Experiment (MC3E) took place during the spring of 2011 centered in north-central Oklahoma, USA. The main goal of this field campaign was to capture the dynamical and microphysical characteristics of precipitating convective systems in the US Central Plains. A major component of the campaign was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state with the intent of deriving model forcing data sets. Over the course of the 46-day MC3E campaign, a total of 1362 radiosondes were launched from the enhanced sonde network. This manuscript provides details on the instrumentation used as part of the sounding array, the data processing activities including quality checks and humidity bias corrections and an analysis of the impacts of bias correction and algorithm assumptions on the determination of convective levels and indices. It is found that corrections for known radiosonde humidity biases and assumptions regarding the characteristics of the surface convective parcel result in significant differences in the derived values of convective levels and indices in many soundings. In addition, the impact of including the humidity corrections and quality controls on the thermodynamic profiles that are used in the derivation of a large-scale model forcing data set are investigated. The results show a significant impact on the derived large-scale vertical velocity field illustrating the importance of addressing these humidity biases.

scholarly journals Are we adequately reviewing confusion inducing drugs in patients referred to the memory assessment service?

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S193-S193
Author(s):  
Siddhant Hegde ◽  
Rashi Negi ◽  
Hari Shanmugaratnam
Keyword(s):  
Quality Improvement ◽  
Cognitive Impairment ◽  
Royal College ◽  
Support Worker ◽  
Short Term ◽  
Data Set ◽  
Memory Assessment ◽  
Dementia Syndrome ◽  
Diagnosis Of Dementia ◽  
The Impact

AimsThe aim of this quality improvement evaluation project is to establish the standard of current practice in relation to reviewing confusion inducing drugs (CIDs) at the time of referral, as it has been hypothesised that these medications contribute to short term cognitive impairment. This is essential in order to establish the validity of the diagnostic processes of dementia syndrome in the memory assessment services.BackgroundIt has long been established that anti-cholinergic medications (ACMs) have contributed to short-term cognitive impairment in patients taking them. This is compounded with the fact that these medications may be continued without review, for longer than was originally intended. The impact of polypharmacy, subsequent anti-cholinergic burden, and the overlapping presence of delirium, may call into question the validity of a diagnosis of dementia in patients who have not been correctly vetted during the course of their assessment. This quality improvement evaluation aims to assess whether patients’ medications are being reviewed before diagnosing a memory disorder. This is in accordance with guidance set out by the NG97 NICE guidelines, The Royal College of Psychiatrists Memory Service National Accreditation Programme (MSNAP), and the National Institute on Ageing and Alzheimer's Association (NIA-AA).MethodAll new referrals to the memory assessment service during July and August 2019 were systematically reviewed and data extracted from the memory referral document and entries on RIO from first point of contact. The following data were recorded: patient ID, GPCOG/6CIT score, final diagnosis, CID prescriptions and CID review.ResultThe results were collated using a data-set of 216 patients (136 females and 80 males,) of which the mean age was 79 years. It was noted that 36% of patients had not had any sort of cognitive assessment before referral, which identifies an area for improvement. However the most substantial finding was that only 10 patients (5%) had a CID prescription review documented in the RIO notes.ConclusionOur data suggest that in our memory assessment service, only a small proportion of patients are having a documented review of their CIDs prior to diagnosis of dementia. In order to improve this and thus improve compliance with guidelines from the Royal College of Psychiatrists MSNAP and the NIA-AA, measures will be taken to issue each dementia support worker and nurse with a CID prescription review card, which will list those medications to consider and flag for review.

scholarly journals Effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan

2018 ◽  
Vol 78 (5) ◽  
pp. 592-610 ◽  
Author(s):  
Abbas Ali Chandio ◽  
Yuansheng Jiang ◽  
Feng Wei ◽  
Xu Guangshun
Keyword(s):  
Wheat Yield ◽  
Considerable Proportion ◽  
Small Scale ◽  
Small Farms ◽  
Short Term ◽  
Agricultural Credit ◽  
Data Set ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper is to evaluate the impact of short-term loan (STL) vs long-term loan (LTL) on wheat productivity of small farms in Sindh, Pakistan. Design/methodology/approach The econometric estimation is based on cross-sectional data collected in 2016 from 18 villages in three districts, i.e. Shikarpur, Sukkur and Shaheed Benazirabad, Sindh, Pakistan. The sample data set consist of 180 wheat farmers. The collected data were analyzed through different econometric techniques like Cobb–Douglas production function and Instrumental variables (two-stage least squares) approach. Findings This study reconfirmed that agricultural credit has a positive and highly significant effect on wheat productivity, while the short-term loan has a stronger effect on wheat productivity than the long-term loan. The reasons behind the phenomenon may be the significantly higher usage of agricultural inputs like seeds of improved variety and fertilizers which can be transformed into the wheat yield in the same year. However, the LTL users have significantly higher investments in land preparation, irrigation and plant protection, which may lead to higher wheat production in the coming years. Research limitations/implications In the present study, only those wheat farmers were considered who obtained agricultural loans from formal financial institutions like Zarai Taraqiati Bank Limited and Khushhali Bank. However, in the rural areas of Sindh, Pakistan, a considerable proportion of small-scale farmers take credit from informal financial channels. Therefore future researchers should consider the informal credits as well. Originality/value This is the first paper to examine the effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan. This paper will be an important addition to the emerging literature regarding effects of credit studies.

scholarly journals Validation of SCIAMACHY HDO/H<sub>2</sub>O measurements using the TCCON and NDACC-MUSICA networks

2015 ◽  
Vol 8 (4) ◽  
pp. 1799-1818 ◽  
Author(s):  
R. A. Scheepmaker ◽  
C. Frankenberg ◽  
N. M. Deutscher ◽  
M. Schneider ◽  
S. Barthlott ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Hydrological Cycle ◽  
Source Region ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
The Tibetan Plateau ◽  
Wet Season ◽  
Data Set ◽  
The Mean ◽  
The Impact

Abstract. Measurements of the atmospheric HDO/H2O ratio help us to better understand the hydrological cycle and improve models to correctly simulate tropospheric humidity and therefore climate change. We present an updated version of the column-averaged HDO/H2O ratio data set from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The data set is extended with 2 additional years, now covering 2003–2007, and is validated against co-located ground-based total column δD measurements from Fourier transform spectrometers (FTS) of the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC, produced within the framework of the MUSICA project). Even though the time overlap among the available data is not yet ideal, we determined a mean negative bias in SCIAMACHY δD of −35 ± 30‰ compared to TCCON and −69 ± 15‰ compared to MUSICA (the uncertainty indicating the station-to-station standard deviation). The bias shows a latitudinal dependency, being largest (∼ −60 to −80‰) at the highest latitudes and smallest (∼ −20 to −30‰) at the lowest latitudes. We have tested the impact of an offset correction to the SCIAMACHY HDO and H2O columns. This correction leads to a humidity- and latitude-dependent shift in δD and an improvement of the bias by 27‰, although it does not lead to an improved correlation with the FTS measurements nor to a strong reduction of the latitudinal dependency of the bias. The correction might be an improvement for dry, high-altitude areas, such as the Tibetan Plateau and the Andes region. For these areas, however, validation is currently impossible due to a lack of ground stations. The mean standard deviation of single-sounding SCIAMACHY–FTS differences is ∼ 115‰, which is reduced by a factor ∼ 2 when we consider monthly means. When we relax the strict matching of individual measurements and focus on the mean seasonalities using all available FTS data, we find that the correlation coefficients between SCIAMACHY and the FTS networks improve from 0.2 to 0.7–0.8. Certain ground stations show a clear asymmetry in δD during the transition from the dry to the wet season and back, which is also detected by SCIAMACHY. This asymmetry points to a transition in the source region temperature or location of the water vapour and shows the added information that HDO/H2O measurements provide when used in combination with variations in humidity.

GST Pass-through Across Indian Sates: Evidence from Difference-in-Differences

2018 ◽  
Vol 66 (1-2) ◽  
pp. 42-49
Author(s):  
Debashree Das
Keyword(s):  
Ordinary Least Squares ◽  
Rate Of Change ◽  
Control Group ◽  
Short Term ◽  
Difference In Differences ◽  
Data Set ◽  
Consumer Prices ◽  
Pass Through ◽  
The Impact ◽  
Special Category

This article analyses whether there exists any short-term inflationary pressure on Indian economy post Goods and Services Tax (GST) implementation. It was found that the introduction of GST showed no significant effect on the rate of change of consumer price index (CPI). Though, the effect of the GST implementation on consumer prices in India showed no significant change in the short term, the impact needs to monitored and observed for the long term, because the current state of economic conditions may have led to a delayed pass-through of the GST increase into consumer prices. To estimate the pass-through effect on prices due to GST implementation from 1 July 2017, various graphical and statistical methods are used to ascertain whether there has been any significant pass-through of GST on CPI– ordinary least squares (OLS) regression, and difference-in differences (DID) estimation technique has been used. The impact of post- and pre-implementation of GST has been analysed through DID by segregating the data set on the basis of treatment and control groups. The non-special category states have been taken as the treatment group and remaining special category states as control group. The results indicate that there is no significant evidence of upward bias in the CPI post GST implementation; these conventional estimates hold true for all states that were segmented based on revenue distribution and contribution to gross domestic product (GDP). JEL: D78, H20, H22

scholarly journals The impact of burning on lion Panthera leo habitat choice in an African savanna

2013 ◽  
Vol 59 (3) ◽  
pp. 335-339 ◽  
Author(s):  
Stephanie Eby ◽  
Anna Mosser ◽  
Ali Swanson ◽  
Craig Packer ◽  
Mark Ritchie
Keyword(s):  
National Park ◽  
Habitat Choice ◽  
Territory Size ◽  
Short Term ◽  
Data Set ◽  
Burned Areas ◽  
The Impact ◽  
Term Data ◽  
Prey Attraction

Abstract Carnivores play a central role in ecosystem processes by exerting top-down control, while fire exerts bottom-up control in ecosystems throughout the world, yet, little is known about how fire affects short-term carnivore distributions across the landscape. Through the use of a long-term data set we investigated the distribution of lions, during the daytime, in relation to burned areas in Serengeti National Park, Tanzania. We found that lions avoid burned areas despite the fact that herbivores, their prey, are attracted to burned areas. Prey attraction, however, likely results from the reduction in cover caused by burning, that may thereby decrease lion hunting success. Lions also do not preferentially utilize the edges of burned areas over unburned areas despite the possibility that edges would combine the benefit of cover with proximity to abundant prey. Despite the fact that lions avoid burned areas, lion territory size and reproductive success were not affected by the proportion of the territory burned each year. Therefore, burning does not seem to reduce lion fitness perhaps because of the heterogeneity of burned areas across the landscape or because it is possible that when hunting at night lions visit burned areas despite their daytime avoidance of these areas.

scholarly journals Perbandingan Estimasi Radar Cuaca Dopler Baron Terhadap Data Observasi Kejadian Hujan Di Kota Kupang

2020 ◽  
Vol 20 (2) ◽  
pp. 1
Author(s):  
Ni Putu Nonik Prianti ◽  
Roddialek Pollo ◽  
Judi K. Nasjoro ◽  
Sulton Kharisma
Keyword(s):  
Early Warning ◽  
Warning System ◽  
Heavy Rain ◽  
Radar Data ◽  
Extreme Weather ◽  
Disaster Mitigation ◽  
Extreme Weather Events ◽  
Observation Data ◽  
Weather Information ◽  
The Impact

Radar is able to provide information about extreme weather observations in the form of heavy rain, so it is important to find the level of accuracy of the radar in providing extreme weather information. So that with accurate data disaster mitigation can be done by creating an early warning system using radar data in order to minimize the impact that will occur. Comparative analysis of the estimated rainfall events on the radar with surface observation data shows a good level of accuracy, but the blankness of the data on the radar due to damage thus influences the decision making of the forecasters when providing extreme weather information quickly to the public. By knowing the radar accuracy level is quite good in estimating rain events, BMKG can provide weather information in the form of appropriate early warning so that people can anticipate extreme weather events

Evaluation of the impact of assimilating spaceborne (GLM) total lightning data and radar data on short-term forecasts of convective events in the 3DVAR framework

2021 ◽  
Author(s):  
Alexandre Fierro ◽  
Junjun Hu ◽  
Yunheng Wang ◽  
Jidong Gao ◽  
Edward Mansell
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Hybrid Methods ◽  
Radar Data ◽  
Western Hemisphere ◽  
Added Value ◽  
Short Term ◽  
Background Error ◽  
Total Lightning ◽  
The Impact

&lt;p&gt;The GLM instruments aboard the GOES-16 and 17 satellites provides nearly uniform spatiotemporal coverage of total lightning over the Americas and adjacent vast oceanic regions of the western hemisphere. This work summarizes recent efforts from our group at CIMMS/NSSL geared towards the evaluation of the potential added value of assimilating GLM-observed total lightning data on short-term, convection-allowing scale (dx = 2-3 km) forecasts for higher impact weather events. Results using data assimilation (DA) approaches ranging from single deterministic three-dimensional variational (3DVAR) methods applied in real time to experimental ensemble-based VAR hybrid methods (3DEnVAR) will be highlighted.&amp;#160;&lt;br&gt;The lightning data assimilation (DA) scheme in these frameworks follow the same core philosophy wherein background water vapor mass mixing ratio is adjusted (increased) locally at or around observed lightning locations, either throughout the entire atmospheric column or within a fixed, confined layer above the lifted condensation level. Toward a more systematic assimilation of real GLM data, emphasis will be directed toward: (i) sensitivity tests with deterministic 3DVAR experiments aimed at evaluating the impact of the horizontal decorrelation length scale, DA cycling frequency as well the length of the accumulation window for the lightning data, (ii) aggregate statistics from real time CONUS-scale experiments over the Spring 2020 and (iii) preliminary results employing ensemble of 3DEnVARs with hybrid (static + flow dependent) background error covariances.&amp;#160;&lt;br&gt;Aggregate statistical results from all deterministic 3DVAR exercises in (i) and (ii) revealed that the assimilation of either radar (radial wind and reflectivity factor) or total lightning (GLM) resulted in overall notably more skillful, shorter term (0-3 h) forecast of composite reflectivity fields, accumulated rainfall, as well as individual storm tracks &amp;#8211; with optimal skill obtained when both radar and lightning data were assimilated. In (iii) forecast impacts related to the following will be summarized: (1) the respective weights assigned to the flow-dependent component and static components of the background error covariances, (2) the inclusion of three time-level sampling for each member during each cycle and (3) the usage of Gaussian noise coupled with a fixed 3 to 12 h spin-up period prior to the beginning of the cycled 3DVAR.&lt;/p&gt;

Empirical statistical downscaling with EPISODES in Austria

2021 ◽  
Author(s):  
Theresa Schellander-Gorgas ◽  
Frank Kreienkamp ◽  
Philip Lorenz ◽  
Christoph Matulla ◽  
Janos Tordai
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Statistical Downscaling ◽  
Climate Impact ◽  
Coarse Grid ◽  
Climate Projections ◽  
Observation Data ◽  
Short Term ◽  
High Quality ◽  
Data Set

&lt;p&gt;EPISODES is an empirical statistical downscaling (ESD) method, which has been initiated and developed by the German Weather Service (DWD). Having resulted in good evaluation scores for Germany, the methodology it is also set-up and adapted for Austria at ZAMG and, hence, for an alpine territory with complex topography.&lt;/p&gt;&lt;p&gt;ESD methods are sparing regarding computational costs compared to dynamical downscaling models. Due to this advantage ESD can be applied in a short time frame and in a demand-based manner. It enables, e.g., processing ensembles of downscaled climate projections, which can be assessed either as stand-alone data set or to enhance ensembles based on dynamical methods. This helps improve the robustness of climatological statements for the purpose of climate impact research.&lt;/p&gt;&lt;p&gt;Preconditions for achieving high-quality results by EPISODES are long-term, temporally consistent observation data sets and a best possible realistic reproduction of relevant large-scale weather conditions by the GCMs. Given these requirements, EPISODES produces high-quality multivariate and spatially/temporally consistent synthetic time series on regular grids or station locations. The output is provided for daily time steps and, at maximum, for the resolution of underlying observation data.&lt;/p&gt;&lt;p&gt;The EPISODES method consists on mainly two steps: At first stage, univariate time series are produced on a coarse grid based on the analogue method and linear regression. It means that coarse scale atmospheric conditions of each single day as described by the GCM projections are assigned to a selection of at most similar daily weather situations of the observed past. From this selection new values are determined by linear regression for each day.&lt;/p&gt;&lt;p&gt;The second stage of the EPISODES method works like a weather generator. Short-term anomalies based on first stage results, on the one hand, and on observations, on the other hand, are matched selecting the most similar day for all used meteorological parameters and coarse grid points at the same time. Together with the high-resolution climatological background of observations and the climatological shift as described by GCM projections the short-term variability are combined to synthetic daily values for each target grid point. This approach provides the desired characteristics of the downscaled climate projections such as multivariability and spatio-temporal consistency.&lt;/p&gt;&lt;p&gt;Recent EPISODES evaluation results for daily precipitation and daily mean temperature are presented for the Austrian federal territory. Performance of the EPISODES ensemble will also be discussed in relation to existing ensembles based on dynamical methods which have already been widely used in climate impact studies in Austria: EURO-CORDEX and &amp;#214;KS15.&lt;/p&gt;

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