Expansion of a localized population of the introduced Stratiotes aloides (Hydrocharitaceae) in Lough Derg, Ireland

An expansion of an introduced female clone of Stratiotes aloides L. (Water-soldier) was located in a delta region on the western side of Lough Derg, Co Galway (v.c.H15), Ireland in 2007. This population was followed over a thirteen-year period. It was initially located at three adjacent sheltered localities, within sweepback bays on either side of an emerging river and within an adjacent canal. The study involved surface observations later supplemented with aerial images. The shallow water conditions, shelter and the presence of Phragmites australis appear to have supported the early establishment by retaining small S. aloides clusters amongst its stems. These clusters later merged to produce a mainly surface expanding monoculture. This enlarged from less than 1 ha to approximately 3.3 ha to occupy much of the sheltered Rossmore Bay area during this study. Flowers were first noticed in 2008, and during subsequent visits, producing infertile seed-pods. Expansion took place with the production of daughter plants. A small nearby population, 1 km to the east, within an unused harbour, did not produce an emergent phase. A small group in a shallow cut, between the two locations, disappeared during the study. Aerial images from different sources were useful to identify the expansion of the emergent stage due to the distinctive bright green coloration of surface leaves. It is unclear how this plant arrived in Lough Derg, but might have been a garden plant release. Small drifting plants, seen during wintertime, may yet colonize other regions within this lake.

Radiation fog over north India during winter from 1989-2004

Lkkj & bl 'kks/k-i= esa rsjg gokbZ vM~Mksa ds orZeku ekSle laca/kh vk¡dMksa dk mi;ksx djrs gq, mÙkjh Hkkjr esa fofdj.k ;qDr dksgjs dk v/;;u fd;k x;k gSA gky gh ds o"kksZa esa Hkkjr ds mÙkjh Hkkxksa esa dksgjs dh mRifÙk esa cgqr vf/kd o`f) ik;h xbZ gSA pw¡fd bafnjk xk¡/kh vUrjjk"Vªh; ¼vkbZ- th- vkbZ-½ gokbZ vM~Ms dh o"kZ 1997&1998] 1998&1999] 1999&2000] 2000&2001] 2001&2002] 2002&2003 vkSj 2003&2004 ds nkSjku dqy 900 mM+kuksa ds ekxZ cnys x,A blfy, bl gokbZ vM~Ms ds oSekfudh izpkyuksa ij iM+s dksgjs ds izfrdwy izHkko dk v/;;u fd;k x;k gSA bafnjk xk¡/kh gokbZ vM~Ms ij dSV&I, dSV&II vkSj dSV&III izpkyuksa ds fy, foekuksa dks mrkjus esa lgk;d vR;f/kd l{ke midj.k iz.kkyh vkbZ- ,y- ,l- miyC/k djkbZ xbZ gSA bl 'kks/k&i= esa xr iUnzg o"kksZa ds LdksiksxzkQksa ifjdfyr de n`’;rk okys izpkyuksa ds fy, vko’;d juos dh n`’; jsatksa vkj- oh- vkj- dh mi;ksfxrk ij fopkj&foe’kZ fd;k x;k gSA mixzg }kjk dksgjs ij fd, x, izs{k.kksa dk /kjkryh; izs{k.kksa ds lkFk lgh rkyesy ik;k x;k gSA mixzg ls izkIr gq, fp= bl ckr dk izek.k gSa fd o"kZ 1998&1999 ds nkSjku mŸkjh Hkkjr esa vR;f/kd l?ku dksgjk vjc lkxj esa cus izpaM pØokr ls vR;f/kd ek=k esa vknzZrk ds izokg ds dkj.k cuk FkkA bl 'kks/k-i= esa bafnjk xk¡/kh gokbZ vM~Mk] y[kuÅ gokbZ vM~Mk] okjk.klh gokbZ vM~Mk vkSj ve`rlj gokbZ vM~Mk ij dksgjs ds nkSjku vf/kdre rkieku vkSj lkisf{kd vknzZrk dh folaxfr;ksa ds e/; laca/k dk irk yxkus dk Hkh iz;kl fd;k x;k gSA Radiation fog over north India has been studied using current weather data of thirteen airports. There has been a tremendous increase in the fog formation over northern parts of India in recent years. An attempt has been made to study the adverse impact of fog on aeronautical operations at Indira Gandhi International (I.G.I.) airport as total number of flights diverted during 1997-98, 1998-99, 1999-2000, 2000-01, 2001-02, 2002-03 and 2003-04 were about 900. I.G.I. airport is provided with a very efficient Instrument Landing System (ILS) for Cat-I, Cat-II and Cat-III operations. The utility of Runway Visual Ranges (RVRs) required for low visibility operations, calculated from skopographs, for the last fifteen years, has been discussed. Satellite observations on fog have been found to be in fair agreement with the surface observations. Most catastrophic fog formations, which occurred over north India during 1998-99, were found to be due to the enormous amount moisture flow from a severe cyclone formed in the Arabian Sea as evidenced in satellite imagery. An attempt has also been made to establish a relation of maximum temperature and Relative Humidity anomaly with the duration of fog at I.G.I. airport, Lucknow airport, Varanasi airport and Amritsar airport.

Data Fusion of GEO FY4A GIIRS and LEO Hyperspectral Infrared Sounders with Surface Observations: A Hong Kong Case Study

Hyperspectral infrared satellite observations from geostationary platforms allow for the retrieval of temperature and water vapor measurements with higher temporal and vertical resolution than was previously available. The Chinese satellite, FY-4A includes the Geostationary Interferometric Infrared Sounder (GIIRS) which has the ability to measure vertical profiles of temperature and water vapor from space at times when ground-based upper air soundings are not available and can fill an important need in short-range weather prediction. In this study, CAPE and LI, which are used for forecasting atmospheric instability, were computed using the SHARPpy algorithm used by the NWS Storm Prediction Center. However, remote infrared and microwave sensing is lacking detailed information in the boundary layer, so the addition of the NOAA MADIS surface data may be necessary in order to get accurate temperature and moisture measurement near the surface. This study uses May 10-16, 2019 in the coastal region near Hong Kong for evaluating the use of hourly surface observations combined with satellite soundings from FY4A GIIRS at two hour intervals. The GIIRS plus MADIS surface-based CAPE and LI estimates are compared to estimates derived from low earth orbiting (LEO) SNPP and NOAA20 from NOAA, METOP from EUMETSAT, NWP reanalysis, and local radiosondes. In the case study, the two-hour sampling interval of the GIIRS geostationary sounder was able to capture the rapid transition (16 hours) from stable to unstable atmosphere in both CAPE and LI. The use of surface observations with satellite soundings gave mixed results, possibly due to the complex terrain near Hong Kong.

Basal stress controls ice-flow variability during a surge cycle of Hagen Bræ, Greenland

Basal conditions play an essential role in the dynamics of outlet glaciers, but direct observations at the bed of glaciers are challenging to obtain. Instead, inverse methods can be used to infer basal parameters from surface observations. Here, we use a simple ice-flow model as a forward model in an inversion scheme to retrieve the spatio-temporally variable basal stress parameter for Hagen Bræ, North Greenland, from 1990 to 2020. Hagen Bræ is a surge-type glacier with up to an order of magnitude variability of winter velocities near the grounding line. We find that downstream changes in the basal stress parameter can explain most of the variation of flow velocity, and we further identify a region of high resistance ~20–40 km from the grounding line. We hypothesise that this region of high resistance plays an important role in controlling glacier discharge.

FYRE Climate: a high-resolution reanalysis of daily precipitation and temperature in France from 1871 to 2012

Surface observations are usually too few and far between to properly assess multidecadal variations at the local scale and characterize historical local extreme events at the same time. A data assimilation scheme has been recently presented to assimilate daily observations of temperature and precipitation into downscaled reconstructions from a global extended reanalysis through an Ensemble Kalman fitting approach and to derive high-resolution fields. Recent studies also showed that assimilating observations at high temporal resolution does not guarantee correct multidecadal variations. The current paper thus proposes (1) to apply the data assimilation scheme over France and over the 1871–2012 period based on the SCOPE Climate reconstructions background dataset and all available daily historical surface observations of temperature and precipitation, (2) to develop an assimilation scheme at the yearly timescale and to apply it over the same period and lastly, (3) to derive the FYRE Climate reanalysis, a 25-member ensemble hybrid dataset resulting from the daily and yearly assimilation schemes, spanning the whole 1871–2012 period at a daily and 8 km resolution over France. Assimilating daily observations only allows reconstructing accurately daily characteristics, but fails in reproducing robust multidecadal variations when compared to independent datasets. Combining the daily and yearly assimilation schemes, FYRE Climate clearly performs better than the SCOPE Climate background in terms of bias, error, and correlation, but also better than the Safran reference surface reanalysis over France available from 1958 onward only. FYRE Climate also succeeds in reconstructing both local extreme events and multidecadal variability. It is freely available at https://doi.org/10.5281/zenodo.4005573 (precipitation, Devers et al., 2020b) and https://doi.org/10.5281/zenodo.4006472 (temperature, Devers et al., 2020c).

A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application

Top-down atmospheric inversion infers surface-atmosphere fluxes from spatially distributed observations of atmospheric compositions, which is a vital means for quantifying large-scale anthropogenic and natural emissions. In this study, we developed a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) based on the Weather Research and Forecasting/Community Multiscale Air Quality Modeling System (WRF/CMAQ) model, the three-dimensional variational (3DVAR) algorithm and the ensemble square root filter (EnSRF) algorithm. It is capable to simultaneously assimilate spatially distributed hourly in-situ measurements of CO, SO2, NO2, PM2.5 and PM10 concentrations to quantitatively optimize gridded emissions of CO, SO2, NOx, primary PM2.5 (PPM2.5) and coarse PM10 (PMC) on regional scale. RAPAS includes two subsystems, initial field assimilation (IA) subsystem and emission inversion (EI) subsystem, which are used to generate a "perfect" chemical initial condition (IC), and conduct inversions of anthropogenic emissions, respectively. A "two-step" inversion scheme is adopted in the EI subsystem in its each data assimilation (DA) window, in which the emission is inferred in the first step, and then, it is input into the CMAQ model to simulate the initial field of the next window, meanwhile, it is also transferred to the next window as the prior emission. The chemical IC is optimized through the IA subsystem, and the original emission inventory is only used in the first DA window. Besides, a "super-observation" approach is implemented based on optimal estimation theory to decrease the computational costs and observation error correlations and reduce the influence of representativeness errors. With this system, we estimated the emissions of CO, SO2, NOx, PPM2.5 and PMC in December 2016 over China using the corresponding nationwide surface observations. The 2016 Multi-resolution Emission Inventory for China (MEIC 2016) was used as the prior emission. The system was run from 26 November to 31 December, in which the IA subsystem was run in the first 5 days, and the EI subsystem was run in the following days. The optimized ICs at the first 5 days and the posterior emissions in December were evaluated against the assimilated and independent observations. Results showed that the root mean squared error (RMSE) decreased by 50.0–73.2%, and the correlation coefficient (CORR) increased to 0.78–0.92 for the five species compared to the simulations without 3DVAR. Additionally, the RMSE decreased by 40.1–56.3 %, and the CORR increased to 0.69–0.87 compared to the simulations without optimized emissions. For the whole mainland China, the uncertainties were reduced by 44.4 %, 45.0 %, 34.3 %, 51.8 % and 56.1 % for CO, SO2, NOx, PPM2.5 and PMC, respectively. Overall, compared to the prior emission (MEIC 2016), the posterior emissions increased by 129 %, 20 %, 5 %, and 95 % for CO, SO2, NOx and PPM2.5, respectively, indicating that there was significant underestimation in the MEIC inventory. The posterior PMC emissions, including anthropogenic and natural dust contributions, increased by 1045 %. A series of sensitivity tests were conducted with different inversion processes, prior emissions, prior uncertainties, and observation errors. Results showed that the "two-step" scheme clearly outperformed the simultaneous assimilation of ICs and emissions ("one-step" scheme), and the system is rather robust in estimating the emissions using the nationwide surface observations over China. Our study offers a useful tool for accurately quantifying multi-species anthropogenic emissions at large scales and near-real time.

Snowfall model validation using surface observations and an optimal estimation snowfall retrieval

In the winter, orographic precipitation falls as snow in the mid to high latitudes where it causes avalanches, affects local infrastructure, or leads to flooding during the spring thaw. We present a technique to validate operational numerical weather prediction model simulations in complex terrain. The presented verification technique uses a combined retrieval approach to obtain surface snowfall accumulation and vertical profiles of snow water at the Haukeliseter test site, Norway. Both surface observations and vertical profiles of snow are used to validate model simulations from the Norwegian Meteorological Institute’s operational forecast system and two simulations with adjusted cloud microphysics.Retrieved surface snowfall is validated against measurements conducted with a double-fence automated reference gauge (DFAR). In comparison, the optimal estimation snowfall retrieval produces + 10.9% more surface snowfall than the DFAR. The predicted surface snowfall from the operational forecast model and two additional simulations with microphysical adjustments (CTRL and ICE-T) are overestimated at the surface with +41.0 %, +43.8 %, and +59.2 %, respectively. Simultaneously, the CTRL and ICE-T simulations underestimate the mean snow water path by -1071.4% and -523.7 %, respectively.The study shows that we would reach false conclusions only using surface accumulation or vertical snow water content profiles. These results highlight the need to combine ground-based in-situ and vertically-profiling remote sensing instruments to identify biases in numerical weather prediction.