Exploring the Assimilation of GLM-Derived Water Vapor Mass in a Cycled 3DVAR Framework for the Short-Term Forecasts of High-Impact Convective Events

2020 ◽  
Vol 148 (3) ◽  
pp. 1005-1028 ◽  
Author(s):  
Junjun Hu ◽  
Alexandre O. Fierro ◽  
Yunheng Wang ◽  
Jidong Gao ◽  
Edward R. Mansell
Keyword(s):  
Water Vapor ◽  
Time Window ◽  
Radar Data ◽  
The United States ◽  
Length Scale ◽  
Mixing Ratio ◽  
Short Term ◽  
Forecast Performance ◽  
Water Vapor Mixing Ratio ◽  
The Impact

Abstract The recent successful deployment of the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite R series (GOES-16/17) provides nearly uniform spatiotemporal measurements of total lightning (intracloud plus cloud to ground) over the Americas and adjacent vast oceanic regions. This study evaluates the potential value of assimilating GLM-derived water vapor mixing ratio on short-term (≤6 h), cloud-scale (dx = 1.5 km) forecasts of five severe weather events over the Great Plains of the United States using a three-dimensional variational (3DVAR) data assimilation (DA) system. Toward a more systematic assimilation of real GLM data, this study conducted sensitivity tests aimed at evaluating the impact of the horizontal decorrelation length scale, DA cycling frequency, and the time window size for accumulating GLM lightning observations prior to the DA. Forecast statistics aggregated over all five cases suggested that an optimal forecast performance is obtained when lightning measurements are accumulated over a 10-min interval and GLM-derived water vapor mixing ratio values are assimilated every 15 min with a horizontal decorrelation length scale of 3 km. This suggested configuration for the GLM DA together with companion experiments (i) not assimilating any data, (ii) assimilating radar data only, and (iii) assimilating both GLM and radar data were evaluated for the same five cases. Overall, GLM data have shown potential to help improve the short-term (<3 h) forecast skill of composite reflectivity fields and individual storm tracks. While this result also held for accumulated rainfall, longer-term (≥3 h) forecasts were generally characterized by noteworthy wet biases.

Variational Assimilation of Radar Data and GLM Lightning-Derived Water Vapor for the Short-Term Forecasts of High-Impact Convective Events

2019 ◽  
Vol 147 (11) ◽  
pp. 4045-4069 ◽  
Author(s):  
Alexandre O. Fierro ◽  
Yunheng Wang ◽  
Jidong Gao ◽  
Edward R. Mansell
Keyword(s):  
Water Vapor ◽  
Radar Data ◽  
The United States ◽  
High Impact ◽  
Added Value ◽  
Mixing Ratio ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Vapor Mass

Abstract The assimilation of water vapor mass mixing ratio derived from total lightning data from the Geostationary Lightning Mapper (GLM) within a three-dimensional variational (3DVAR) system is evaluated for the analysis and short-term forecast (≤6 h) of a high-impact convective event over the northern Great Plains in the United States. Building on recent work, the lightning data assimilation (LDA) method adjusts water vapor mass mixing ratio within a fixed layer depth above the lifted condensation level by assuming nearly water-saturated conditions at observed lightning locations. In this algorithm, the total water vapor mass added by the LDA is balanced by an equal removal outside observed lightning locations. Additional refinements were also devised to partially alleviate the seasonal and geographical dependence of the original scheme. To gauge the added value of lightning, radar data (radial velocity and reflectivity) were also assimilated with or without lightning. Although the method was evaluated in quasi–real time for several high-impact weather events throughout 2018, this work will focus on one specific, illustrative severe weather case wherein the control simulation—which did not assimilate any data—was eventually able to initiate and forecast the majority of the observed storms. Given a relatively reasonable forecast in the control experiment, the GLM and radar assimilation experiments were still able to improve the short-term forecast of accumulated rainfall and composite radar reflectivity further, as measured by neighborhood-based metrics. These results held whether the simulations made use of one single 3DVAR analysis or high-frequency (10 min) successive cycling over a 1-h period.

scholarly journals Intercomparison of in situ water vapor balloon-borne measurements from Pico-SDLA H<sub>2</sub>O and FLASH-B in the tropical UTLS

2015 ◽  
Vol 8 (12) ◽  
pp. 13693-13727
Author(s):  
M. Ghysels ◽  
E. D. Riviere ◽  
S. Khaykin ◽  
C. Stoeffler ◽  
N. Amarouche ◽  
...  
Keyword(s):  
Water Vapor ◽  
Low Cost ◽  
Deep Convection ◽  
Mixing Ratio ◽  
Stratospheric Water Vapor ◽  
In Situ Techniques ◽  
Scientific Project ◽  
Water Vapor Mixing Ratio ◽  
The Impact

Abstract. In this paper we compare water vapor mixing ratio measurements from two quasi-parallel flights of the Pico-SDLA H2O and FLASH-B hygrometers. The measurements were made on 10 February 2013 and 13 March 2012, respectively, in the tropics near Bauru, Sao Paulo St., Brazil during an intense convective period. Both flights were performed as part of a French scientific project, TRO-Pico, to study the impact of the deep-convection overshoot on the water budget. Only a few instruments that permit the frequent sounding of stratospheric water vapor can be flown within a small volume weather balloons. Technical difficulties preclude the accurate measurement of stratospheric water vapor with conventional in situ techniques. The instruments described here are simple and lightweight, which permits their low-cost deployment by non-specialists aboard a small weather balloon. We obtain mixing ratio retrievals which agree above the cold-point tropopause to within 1.9 and 0.5 % for the first and second flights, respectively. This level of agreement for measured stratospheric water mixing ratio is among the best ever reported in the literature. Because both instruments show similar profiles within their combined uncertainties, we conclude that the Pico-SDLA H2O and FLASH-B datasets are mutually consistent.

scholarly journals Long-term trends of the concentration of the minor constituents in the mesosphere – a model study

2007 ◽  
Vol 7 (6) ◽  
pp. 15453-15494 ◽  
Author(s):  
M. Grygalashvyly ◽  
G. R. Sonnemann ◽  
P. Hartogh
Keyword(s):  
Water Vapor ◽  
Middle Atmosphere ◽  
Co2 Concentration ◽  
Small Time ◽  
Vapor Concentration ◽  
Time Range ◽  
Mixing Ratio ◽  
Model Calculations ◽  
Water Vapor Mixing Ratio ◽  
The Impact

Abstract. We investigate the influence of the rising concentrations of methane, dinitrogen oxide and carbon dioxide since the pre-industrial era upon the chemistry of the mesosphere. We use for calculations our global 3D-model COMMA-IAP designed for the exploration of the MLT-region and particularly the extended mesopause region. In order to get approximated data of the solar Lyman-α flux back to the pre-industrial time, we derived a quadratic fit using the sunspot number available since 1749 as the only solar proxy for the Lyman-α flux before 1947. The Lyman-α flux values are employed to determine the water vapor dissociation rate. The water vapor trend analysis utilizes estimated methane trends since the pre-industrial era. An unsolved problem for the model calculations consists of the water vapor mixing ratio at the hygropause during the time range of trend calculation. We assume that the hygropause was dryer at the pre-industrial time than currently. As a consequence of the methane oxidation, the middle atmosphere became more humid according to the rising methane concentration, but depending on height and with a small time delay of few years. The solar influence on the water vapor mixing ratio is insignificant below about 80 km within summery high latitudes, but it becomes increasingly more important above this altitude. The growing water vapor concentration increases the hydrogen radical concentration and reduces the mesospheric ozone. A second region of stronger ozone decrease is located in the vicinity of the stratopause. Increasing CO2 concentration enhances slightly the concentration of CO in the mesosphere, but its influence upon the chemistry is small and its main effect is connected with a cooling of the upper atmosphere. We discuss the trends particularly in view of the impact on the NLC region.

scholarly journals Intercomparison of in situ water vapor balloon-borne measurements from Pico-SDLA H<sub>2</sub>O and FLASH-B in the tropical UTLS

2016 ◽  
Vol 9 (3) ◽  
pp. 1207-1219 ◽  
Author(s):  
Mélanie Ghysels ◽  
Emmanuel D. Riviere ◽  
Sergey Khaykin ◽  
Clara Stoeffler ◽  
Nadir Amarouche ◽  
...  
Keyword(s):  
Water Vapor ◽  
Low Cost ◽  
Deep Convection ◽  
Data Sets ◽  
Mixing Ratio ◽  
Stratospheric Water Vapor ◽  
In Situ Techniques ◽  
Water Vapor Mixing Ratio ◽  
The Impact

Abstract. In this paper we compare water vapor mixing ratio measurements from two quasi-parallel flights of the Pico-SDLA H2O and FLASH-B hygrometers. The measurements were made on 10 February 2013 and 13 March 2012, respectively, in the tropics near Bauru, São Paulo state, Brazil during an intense convective period. Both flights were performed as part of a French scientific project, TRO-Pico, to study the impact of the deep-convection overshoot on the water budget. Only a few instruments that permit the frequent sounding of stratospheric water vapor can be flown within small-volume weather balloons. Technical difficulties preclude the accurate measurement of stratospheric water vapor with conventional in situ techniques. The instruments described here are simple and lightweight, which permits their low-cost deployment by non-specialists aboard a small weather balloon. We obtain mixing ratio retrievals which agree above the cold-point tropopause to within 1.9 and 0.5 % for the first and second flights, respectively. This level of agreement for balloon-borne measured stratospheric water mixing ratio constitutes one of the best agreement reported in the literature. Because both instruments show similar profiles within their combined uncertainties, we conclude that the Pico-SDLA H2O and FLASH-B data sets are mutually consistent.

scholarly journals Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method

2021 ◽  
Vol 13 (16) ◽  
pp. 3090
Author(s):  
Peng Liu ◽  
Yi Yang ◽  
Anwei Lai ◽  
Yunheng Wang ◽  
Alexandre O. Fierro ◽  
...  
Keyword(s):  
Water Vapor ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
Horizontal Resolution ◽  
Wind Fields ◽  
Short Term ◽  
Background Error ◽  
Forecast Performance ◽  
Total Lightning

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event.

Abstract P164: The Impact of the Expanded EVT Window on Acute Stroke Services: A Large Northwest Telestroke Network Experience

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Theodore Lowenkopf ◽  
Leslie Corless ◽  
Elizabeth Baraban
Keyword(s):  
Acute Stroke ◽  
Rural Areas ◽  
Treatment Time ◽  
Time Window ◽  
Tertiary Care ◽  
The United States ◽  
Geographic Region ◽  
Anterior Circulation ◽  
Stroke Subtypes ◽  
The Impact

Background: Telestroke has led the technological revolution in providing acute medical services to rural areas in the United States since the beginning of this century. In January 2018 the American Stroke Association made a level IA recommendation to expand the treatment time window for endovascular thrombectomy (EVT) for acute ischemic stroke (AIS) from 6 to 24 hours for anterior circulation stroke based on perfusion imaging. Our study is the first to our knowledge to report the effect of the expanded time window on acute stroke consult and treatment volumes in a large rural supporting telestroke network. Methods: Stroke registry data from two tertiary care facilities from a 22 hospital telestroke network supporting a large (> 78,000 mi 2 ) primarily rural Northwest geographic region were used. Data included stroke patients arriving within 24 hours of last known well (LKW) between January 2017 and March 2019. Patients arriving January 2017 to December 2017 were grouped into the PRE-expanded time window and those arriving April 2018 to March 2019 into the POST-expanded time window. Stroke subtypes, transfers, telestroke consults (via phone or video), and EVT treatments were compared across time periods. Analyses were performed using Pearson’s chi square test, corrected for multiple comparisons. Results: A total of 1117 patients arrived with stroke symptoms within 24 hours of LKW, 567 (50.8%) in PRE and 550 (49.2%) in POST-window. The percentage of all stroke subtypes were not significantly different in the PRE and POST patient groups (p=.720). However, the percent of telestroke consults increased by 12.1% from 62.3% to 74.4% (p<.001) but the percent of video consults remained similar (25.9% vs 25.8%). The total number of transfers (142 vs 141) and percentage of transfers among AIS patients (25.0% vs 25.6%) from partner to hub did not change. The percentage of thrombectomies among transfers rose by 8.7% with the expanded time window, but was not statistically significant [p=0.118]. Conclusions: In a large Northwest telestroke rural network the expanded EVT treatment time window led to a marked increase in all telestroke consults but did not impact video consults, transfer, or percentage of patients treated.

Subcloud and Cloud-Base Latent Heat Fluxes during Shallow Cumulus Convection

2019 ◽  
Vol 77 (3) ◽  
pp. 1081-1100 ◽  
Author(s):  
Neil P. Lareau
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Latent Heat ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Cloud Base ◽  
Cumulus Convection ◽  
Mixing Ratio ◽  
Convective Boundary ◽  
Water Vapor Mixing Ratio

Abstract Doppler and Raman lidar observations of vertical velocity and water vapor mixing ratio are used to probe the physics and statistics of subcloud and cloud-base latent heat fluxes during cumulus convection at the ARM Southern Great Plains (SGP) site in Oklahoma, United States. The statistical results show that latent heat fluxes increase with height from the surface up to ~0.8Zi (where Zi is the convective boundary layer depth) and then decrease to ~0 at Zi. Peak fluxes aloft exceeding 500 W m−2 are associated with periods of increased cumulus cloud cover and stronger jumps in the mean humidity profile. These entrainment fluxes are much larger than the surface fluxes, indicating substantial drying over the 0–0.8Zi layer accompanied by moistening aloft as the CBL deepens over the diurnal cycle. We also show that the boundary layer humidity budget is approximately closed by computing the flux divergence across the 0–0.8Zi layer. Composite subcloud velocity and water vapor anomalies show that clouds are linked to coherent updraft and moisture plumes. The moisture anomaly is Gaussian, most pronounced above 0.8Zi and systematically wider than the velocity anomaly, which has a narrow central updraft flanked by downdrafts. This size and shape disparity results in downdrafts characterized by a high water vapor mixing ratio and thus a broad joint probability density function (JPDF) of velocity and mixing ratio in the upper CBL. We also show that cloud-base latent heat fluxes can be both positive and negative and that the instantaneous positive fluxes can be very large (~10 000 W m−2). However, since cloud fraction tends to be small, the net impact of these fluxes remains modest.

scholarly journals Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments

2018 ◽  
Vol 146 (1) ◽  
pp. 175-198 ◽  
Author(s):  
Rong Kong ◽  
Ming Xue ◽  
Chengsi Liu
Keyword(s):  
Data Assimilation ◽  
System Simulation ◽  
Radar Data ◽  
Mixing Ratio ◽  
State Variables ◽  
Ensemble Size ◽  
Simulation Experiments ◽  
Background Error ◽  
Variational Minimization ◽  
Water Vapor Mixing Ratio

Abstract A hybrid ensemble–3DVar (En3DVar) system is developed and compared with 3DVar, EnKF, “deterministic forecast” EnKF (DfEnKF), and pure En3DVar for assimilating radar data through perfect-model observing system simulation experiments (OSSEs). DfEnKF uses a deterministic forecast as the background and is therefore parallel to pure En3DVar. Different results are found between DfEnKF and pure En3DVar: 1) the serial versus global nature and 2) the variational minimization versus direct filter updating nature of the two algorithms are identified as the main causes for the differences. For 3DVar (EnKF/DfEnKF and En3DVar), optimal decorrelation scales (localization radii) for static (ensemble) background error covariances are obtained and used in hybrid En3DVar. The sensitivity of hybrid En3DVar to covariance weights and ensemble size is examined. On average, when ensemble size is 20 or larger, a 5%–10% static covariance gives the best results, while for smaller ensembles, more static covariance is beneficial. Using an ensemble size of 40, EnKF and DfEnKF perform similarly, and both are better than pure and hybrid En3DVar overall. Using 5% static error covariance, hybrid En3DVar outperforms pure En3DVar for most state variables but underperforms for hydrometeor variables, and the improvement (degradation) is most notable for water vapor mixing ratio qυ (snow mixing ratio qs). Overall, EnKF/DfEnKF performs the best, 3DVar performs the worst, and static covariance only helps slightly via hybrid En3DVar.

scholarly journals A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals

2019 ◽  
Vol 12 (7) ◽  
pp. 3943-3961 ◽  
Author(s):  
Ali Jalali ◽  
Shannon Hicks-Jalali ◽  
Robert J. Sica ◽  
Alexander Haefele ◽  
Thomas von Clarmann
Keyword(s):  
Water Vapor ◽  
A Priori ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Mixing Ratio ◽  
A Priori Information ◽  
Fine Grid ◽  
Large Power ◽  
Priori Information ◽  
Water Vapor Mixing Ratio

Abstract. Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

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