Search

In times of the COVID-19 pandemic, teachers on all levels have had to adapt to an online or hybrid teaching environment. People in geography, a discipline that traditionally values field trips to connect theory to practice, have had to find online alternatives for educational activities that normally would have taken place in the field. This has led to several innovative practices, which, however, have only to a limited degree been purposively tested for efficacy because of the ad-hoc, enforced nature of the required changes. This project deals with this issue by studying, through student workshops dealing with the creation of online didactic walking routes in two cities, how students can obtain specific geographical competences such as interpreting different historical layers that collectively shape the current urban fabric through online cartography. We found that students reported clear improvements in geographical reasoning skills, regarding both GIS and heritage interpretation. There were no clear patterns regarding the role of familiarity with the studied city for the quality of the produced story maps. On final reflection, we argue that online cartographic exercises are a valuable addition to the geographers' educational toolkit to bounce forward to a more resilient, reflective educational practice after the pandemic.

Mesoscale weather systems cause spatiotemporal variability in offshore wind power, and insight into their fluctuations can support grid operations. In this study, a 10-year model integration with the kilometre-scale atmospheric model COnsortium for Small-scale MOdelling – CLimate Mode (COSMO-CLM) provided a wind and potential power fluctuation analysis in the Kattegat, a midlatitude sea strait with a width of 130 km and an irregular coastline. The model agrees well with scatterometer data away from coasts and small islands, with a spatiotemporal root-mean square difference of 1.35 m s−1. A comparison of 10 min wind speed at about 100 m with lidar data for a 2-year period reveals very good performance, with a slight model overestimation of 0.08 m s−1 and a high value for the Perkins skill score (0.97). From periodograms made using the Welch's method, it was found that the wind speed variability on a sub-hourly timescale is higher in winter compared to summer. In contrast, the wind power varies more in summer when winds often drop below the rated power threshold. During winter, variability is largest in the northeastern part of the Kattegat due to a spatial spin-up of convective systems over the sea during the predominant southwesterly winds. Summer convective systems are found to develop over land, driving spatial variability in offshore winds during this season. On average over the 10 summers, the mesoscale wind speeds are up to 20 % larger than the synoptic background at 17:00 UTC with a clear diurnal cycle. The winter-averaged mesoscale wind component is up to 10 % larger, with negligible daily variation. Products with a lower resolution like ERA5 substantially underestimate this ratio between the mesoscale and synoptic wind speed. Moreover, taking into account mesoscale spatial variability is important for correctly representing temporal variability in power production. The root-mean square difference between two power output time series, one ignoring and one accounting for mesoscale spatial variability, is 14 % of the total power generation.

AbstractTwo decades of high-resolution satellite observations and climate modeling studies have indicated strong ocean–atmosphere coupled feedback mediated by ocean mesoscale processes, including semipermanent and meandrous SST fronts, mesoscale eddies, and filaments. The air–sea exchanges in latent heat, sensible heat, momentum, and carbon dioxide associated with this so-called mesoscale air–sea interaction are robust near the major western boundary currents, Southern Ocean fronts, and equatorial and coastal upwelling zones, but they are also ubiquitous over the global oceans wherever ocean mesoscale processes are active. Current theories, informed by rapidly advancing observational and modeling capabilities, have established the importance of mesoscale and frontal-scale air–sea interaction processes for understanding large-scale ocean circulation, biogeochemistry, and weather and climate variability. However, numerous challenges remain to accurately diagnose, observe, and simulate mesoscale air–sea interaction to quantify its impacts on large-scale processes. This article provides a comprehensive review of key aspects pertinent to mesoscale air–sea interaction, synthesizes current understanding with remaining gaps and uncertainties, and provides recommendations on theoretical, observational, and modeling strategies for future air–sea interaction research.Significance StatementRecent high-resolution satellite observations and climate models have shown a significant impact of coupled ocean–atmosphere interactions mediated by small-scale (mesoscale) ocean processes, including ocean eddies and fronts, on Earth’s climate. Ocean mesoscale-induced spatial temperature and current variability modulate the air–sea exchanges in heat, momentum, and mass (e.g., gases such as water vapor and carbon dioxide), altering coupled boundary layer processes. Studies suggest that skillful simulations and predictions of ocean circulation, biogeochemistry, and weather events and climate variability depend on accurate representation of the eddy-mediated air–sea interaction. However, numerous challenges remain in accurately diagnosing, observing, and simulating mesoscale air–sea interaction to quantify its large-scale impacts. This article synthesizes the latest understanding of mesoscale air–sea interaction, identifies remaining gaps and uncertainties, and provides recommendations on strategies for future ocean–weather–climate research.

As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km−2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses.

This article describes recent advances in the capability of new satellite sensors for observing Tropical Cyclones (TC) fine structure, wind field, and temporal evolution. The article is based on a World Meteorological Organization (WMO) report prepared for the 10th International Workshop on Tropical Cyclones (IWTC), held in Bali in December 2022, and its objective is to present updates in TC research and operation every four years. Here we focus on updates regarding the most recent space-based TC observations, and we cover new methodologies and techniques using polar orbiting sensors, such as C-band synthetic aperture radars (SARs), L-band and combined C/X-band radiometers, scatterometers, and microwave imagers/sounders. We additionally address progress made with the new generation of geostationary and small satellites, and discuss future sensors planned to be launched in the next years. We then briefly describe some examples on how the newest sensors are used in operations and data assimilation for TC forecasting and research, and conclude the article with a discussion on the remaining challenges of TC space-based observations and possible ways to address them in the near future.

IntroductionThe genetic diversity of Robusta coffee (Coffea canephora), a cornerstone in the global coffee industry, remains not fully explored, leading to a significant gap in our understanding of its sensory intricacies. Our study evaluated the sensory quality potential of the Robusta cultivars from the INERA Coffee Collection in Yangambi (the Democratic Republic of the Congo), the local wild diversity, and their hybrids.MethodsWe evaluated the sensory attributes of 70 genotypes representing the genetic structure of the coffee collection. Of those 70, 22 genotypes were evaluated for two consecutive years to assess the consistency of the sensory quality. Standard coffee cupping with the Fine Robusta Standards and Protocols was enhanced through sensory descriptors from the Coffee Taster’s Flavor Wheel. Each genotype’s sensory profile was constructed based on the Total cupping score and the frequency of reported sensory descriptors. The Total cupping score ranged from 75.75 to 84.75, with a substantial variation in sensory profiles, even within a genetic cluster.Results and discussionNutty/Cocoa was the most frequently reported descriptor class. The sensory profile ideotype exhibits a high frequency of Fruity, Sweet, and Sour/Fermented descriptors and a low frequency of Green/Vegetative, Other, and Roasted descriptors. Evidence suggests that the sensory profile of a genotype is consistent over two harvest years. Genotypes with promising and unique sensory profiles were discovered within the cultivars and the wild – cultivar hybrids. The genetic diversity of wild and cultivated Robusta in the Democratic Republic of the Congo could play an essential role in understanding and improving its sensory quality.

Abstract Tropical forest phenology directly affects regional carbon cycles, but the relation between species‐specific and whole‐canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large‐scale impacts of human‐induced climate change. Ground‐based long‐term (1937–1956) phenological observations of 140 tropical tree species are recovered, species‐specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand‐level canopy dynamics. High phenological variability within and across species and in climate–phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species‐specific decoupling in time along a bi‐modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy‐wide scaling and the interpretation of remotely sensed phenology signals.

WindRAD (Wind Radar) is a dual-frequency rotating fan-beam scatterometer instrument on the FY-3E (FengYun-3E) satellite. Scatterometers are generally calibrated using the linear NOC (NWP Ocean Calibration) method to control the main gain factor of the radar. While WindRAD is stable, the complex geometry, the design of the instrument, and the rotating antenna make the backscatter (σ∘) distributions persistently non-linear; hence NOC is insufficient. Therefore, a higher-order calibration (HOC) method is proposed. The CDF (cumulative distribution function) matching technique is employed to match the CDF of measured σ∘ instances to simulated σ∘ instances. HOC removes the non-linearities for each incidence angle. However, it is not constructed to remove the anomalous harmonic azimuth dependencies caused by the antenna rotation. These azimuth dependencies are reduced by NOCant (NOC as a function of incidence angle and relative antenna azimuth angle). Therefore, the combination of HOC and NOCant is implemented to correct both anomalous σ∘ amplitude and azimuth variations. The wind retrieval performance is evaluated with NOCant, HOC, and HOC and NOCant combined. The wind statistics and the cone distance metric both show that HOC&NOCant achieves the optimal winds for C-band and Ku-band. The calibrations have been tested on two operational input data versions; HOC works well on both data versions and HOC&NOCant can achieve the optimal wind performance for both data versions. This confirms the usefulness of HOC in the case of non-linear instrument gain anomalies.

Abstract Global ultraviolet (UV) surface reflectivity climatologies are all composed from daylight passive remote sensing observations of reflected UV light, generally integrated over a distribution of attainable reflection directions. We discovered the sensitivity of Aeolus lidar surface returns (LSR) to surface characteristics, providing the first evidence that active remote sensing can be effectively used for retrieving unidirectional UV surface reflectivity on global scales. LSR reproduces surface reflectivity monthly changes in Sahara, which are visible in the Lambertian Equivalent Reflectivity (LER) climatologies from TROPOMI and GOME-2. Very high correlations (r > 0.90) between gridded LSR and the LER climatologies are reported at global and regional scales for 36 different regions. Three clear land cover gradients are discerned from Aeolus LSR signal: (1) water/land, (2) vegetation/arid areas and (3) no snow/snow. The strongest LSR signal was retrieved over snow, while over vegetation, we found moderate negative agreement (r

Background and aims – While tropical forests play an important role in carbon sequestration, they are assumed to be sensitive to rising temperatures and prolonged drought. Plant functional traits are useful for understanding and predicting the effects of such changes in plant communities. Here, we analyse the variation of leaf traits of understory woody species of the Congo Basin rainforests over a 60-year period using herbaria as tools and we verify if this variation is potentially related to recent climate change. Material and methods – Leaves of five shrub species were collected in 2019–2022 in Congolese old-growth forests (Yangambi Biosphere Reserve, DR Congo) from different positions on the shrub. These leaves were compared with herbarium specimens collected in the same area before 1960. For both periods, we assessed leaf size, specific leaf area, stomatal size, and stomatal density for all species. Key results – The variability of the functional traits of the understory woody species are independent of the position of the leaves in the crown. This allows for the use of historic herbarium collections for trait analyses on tropical understory shrubs. The traits of the recently collected leaves were notably different from the traits of herbarium leaves collected in pre-1960: recent leaves were significantly larger, had a higher Specific Leaf Area, a smaller stomata pore length, and, apart from Coffea canephora, showed a lower stomatal density. Conclusion – The difference in traits over time is probably related to the increase in temperature and to atmospheric CO2 concentration, as the average temperature at Yangambi over the past 60 years has shown an upward trend consistent with global increasing CO2 levels, while the average annual rainfall has remained unchanged. Our results provide a first insight into the response of forest species to climate change in the Congo Basin forests, and on how the understory species and the ecosystem will react in the long term, when the temperature further increases.

A new ASCAT coastal wind product based on a 12.5 km grid size is presented. The new product contains winds up to the coast line and is identical to the current operational coastal product over the open ocean. It is based on the assumption that within a wind vector cell land and sea have constant radar cross section. With an accurate land fraction calculated from ASCAT’s spatial response function and a detailed land mask, the land correction can be obtained with a simple linear regression. The coastal winds stretch all the way to the coast, filling the coastal gap in the operational coastal ASCAT product, resulting in three times more winds within a distance of 20 km from the coast. The Quality Control (QC), based on the regression error and the regression bias error, reduces this abundance somewhat. A comparison of wind speed pdfs with those from NWP forecasts shows that the influence of land in the land-corrected scatterometer product appears more reasonable and starts not as far offshore as that in the NWP forecasts. The VRMS difference with moored buoys increases slightly from about 2.4 m/s at 20 km or more from the coast to 4.2 m/s at less than 5 km, where coastal wind effects clearly contribute to the latter difference. While the QC based on the regression bias error flags many WVCs that compare well with buoys, the land-corrected coastal product with more abundant coastal winds appears useful for nowcasting and other coastal wind applications.

Accurate knowledge of wind directions plays a critical role in ocean surface wind retrieval and tropical cyclone (TC) research. Under TC conditions, apparent wind streaks induced by marine atmospheric boundary layer rolls can be detected in VV- and VH-polarized synthetic aperture radar (SAR) images. It suggests that though relatively noisy, VH signals may help enhance wind streak orientation magnitudes contained in VV signals and thus to achieve a more accurate wind direction estimation. The study proposes a new method for wind direction retrieval from TC SAR images. Unlike conventional approaches, which calculate wind directions from single-polarization imagery, the method combines VV and VH signals to obtain continuous wind direction maps across moderate and extreme wind speed regimes. The technique is developed based on the histogram of oriented gradient descriptor and Hann window function, accounting for the contribution of neighboring wind streak information (weighted by separation distances). As a case study, the wind directions over four TCs (Karl, Maria, Douglas, and Larry) are derived and verified by estimates from simultaneous dropsonde, ASCAT and ECMWF winds, showing a promising consistency. Furthermore, a more comprehensive statistical analysis is carried out with 14 SAR images, revealing that obtained wind directions have a correlation coefficient of 0.98, a bias of −6.07$^{\circ }$ and a RMSD of 20.24$^{\circ }$, superior to estimates from VV (0.97, −7.84$^{\circ }$, and 24.23$^{\circ }$, resp.) and VH signals (0.96, −10.46$^{\circ }$, and 29.53$^{\circ }$, resp.). The encouraging results prove the feasibility of the technique in SAR wind direction retrieval.

Cultivation of Robusta coffee is likely to gain importance because of its high disease resistance and climate envelope. Robusta coffee genetic resources conserved in field genebanks can play an important role to further improve its cupping quality and other agronomic traits, but such Coffea canephora collections are limited and still poorly characterized. In this study, we characterized the genetic composition of the historically important but until recently neglected INERA Coffee Collection in Yangambi (the Democratic Republic of Congo). We used GBS to discover genome-wide genetic diversity, created and validated a novel multiplex amplicon sequencing (HiPlex) screening assay to genetically screen 730 coffee shrubs of the Yangambi Coffee Collection, grouped clonal material and delineated 263 accessions with unique genetic fingerprints. Comparison to reference material of three genetic origins revealed that the majority of the Yangambi accessions were assigned a ‘Lula’ cultivar origin, four accessions were assigned to Congolese subgroup A and nine accessions were most closely related to local wild accessions. About one-quarter of the accessions was likely derived from hybridization between these groups, which could result from seed-based propagation of the collection, breeding efforts, or natural cross-pollination. Parental analyses discovered eight preferentially used accessions, which may correspond to historically selected founders, or direct descendants thereof, whose seed material was once widely used to establish coffee plantations. Finally, two core collections were proposed using the maximization strategy (CC-I; 100 accessions) and genetic distance method (CC-X; 10 accessions). Our study demonstrates a method for the genetic characterization of Robusta coffee collections in general and contributes to the re-evaluation and exploration of the Robusta coffee genetic resources in the Democratic Republic of the Congo in particular.

Strengths and weakness of remotely sensed winds are discussed, along with the current capabilities for remotely sensing winds and stress. Future missions are briefly mentioned. The observational needs for a wide range of wind and stress applications are provided. These needs strongly support a short list of desired capabilities of future missions and constellations.

Carrying a laser Doppler instrument, the Aeolus satellite was launched in 2018, becoming the first mission for atmospheric wind profile measurements from space. Before utilizing the Aeolus winds for different applications, evaluating their data quality is essential. With the help of ground-based wind profiling radar measurements and the European Centre for Medium-Range Weather Forecasts (ECMWF) model equivalents, this study quantifies the error characteristics of Aeolus L2B (baseline-11) near-real-time horizontal line-of-sight winds across Australia during October 2020–March 2021 by using both inter-comparison and triple collocation analysis. The results of the inter-comparison analysis indicate that both Rayleigh-clear winds and Mie-cloudy winds are in good agreement with the ground-based radar measurements with overall absolute mean biases smaller than 0.7 m s−1 and correlation coefficients larger than or equal to 0.9. Moreover, assuming the radar measurements as the reference data set, Mie-cloudy winds are shown to be more precise than Rayleigh-clear winds with an overall random error of 4.14 and 5.81 m s−1, respectively. Similar results were also found from triple collocation analysis, with error standard deviations of 5.61 and 3.50 m s−1 for Rayleigh-clear winds and Mie-cloudy winds. In addition, the Mie channel is shown to be more capable of capturing the wind in the planetary boundary layer (< 1500 m). The findings of this study demonstrate the good performance of space-borne Doppler lidar for wind profiling and provide valuable information for data assimilation in numerical weather prediction.

Spaceborne microwave sensors, measuring co-/cross-polarization (VV/VH) normalized radar cross section signals, have been widely used for tropical cyclone (TC) monitoring. However, considerable gaps remain to obtain TC intensity since these satellite data either blur inner-core structures (e.g., scatterometer data) or have limited spatial–temporal coverage (e.g., synthetic aperture radar (SAR) data). This study aims to get more accurate TC intensity estimates from scatterometers, which have good global coverage but relatively low spatial resolution. To overcome the blurring effect in scatterometers, we propose a new technique for guidance on TC intensities, with maximum 1-min sustained winds calculated as a function of decay parameters provided by the parametric Rankine-type model. The technique is employed on advanced scatterometer (ASCAT) data acquired between 2016 and 2017, validated with simultaneous SAR VH geophysical model function measurements and best-track (BT) estimates. When validated with BT estimates, the method enhances the blurred maximum winds, where the standard deviation of difference decreased from 6.3 to 3.49 m/s and the coefficient of determination increased from 0.7 to 0.89. Besides, it is noteworthy that the proposed technique performs slightly better than the Mayers–Ruf method. The promising results indicate that the technique can provide more representative TC maximum 1-min sustained wind estimates from ASCAT data, thus contributing to the further exploitation of scatterometer data for TC warnings.

Introduction Optimal collaboration between general practice and hospital care is crucial to maintain affordable and sustainable access to healthcare for the entire population. General practitioners (GPs) are the gatekeepers to specialist care and patients will visit hospitals mostly only after referral. However, a substantial part of these referrals may be inappropriate, as communication between GPs and medical specialists can be challenging and referring patients may be the most obvious action for a GP to perform.A new digital platform (Prisma) connects GPs and specialists in interdisciplinary groups and facilitates asynchronous, accessible and fast teleconsultation within the group. No previous research has been done to evaluate the impact of this new platform on the referral rates to the hospital.Methods and analysis A stepped-wedge randomised controlled trial (RCT) will be performed in Zwolle region in the Netherlands to analyse the effect of introduction of the platform on rate of inappropriate referrals to orthopaedic surgery. In four steps, GPs in the region will be given access to the platform. GPs will be part of the control condition until randomisation to the intervention. According to our sample size calculation, we need to include 18 practices with 1008 patients presenting with hip and knee symptoms. Routine care data of hospital registrations will be analysed to calculate the rate of inappropriate referrals (primary outcome). Secondary outcome are costs, primary and secondary care workload, posted cases and user satisfaction. Alongside this quantitative analysis, we will evaluate patient experience, facilitators and barriers for use of the platform.Ethics and dissemination The medical ethics review board of University Medical Center Groningen (UMCG), the Netherlands (METc-number: 2021/288) has confirmed that the Medical Research Involving Human Subjects Act (WMO) does not apply to the process evaluation because the study does not involve randomisation of patients or different medical treatments (letter number: M21.275351).Trial registration number NL9704.

The planetary boundary layer (PBL) governs the vertical transport of mass, momentum, and moisture between the surface and the free atmosphere, and thus the determination of PBL height (BLH) is recognized as crucial for air quality, weather, and climate analysis. Although reanalysis products can provide important insight into the global view of BLH in a seamless way, the BLH observed in situ on a global scale remains poorly understood due to the lack of high-resolution (1 or 2 s) radiosonde measurements. The present study attempts to establish a near-global BLH climatology at synoptic times (00:00 and 12:00 UTC) and in the daytime using high-resolution radiosonde measurements over 300 radiosonde sites worldwide for the period 2012 to 2019, which is then compared against the BLHs obtained from four reanalysis datasets, including ERA5, MERRA-2, JRA-55, and NCEP-2. The variations in daytime BLH exhibit large spatial and temporal dependence, and as a result the BLH maxima are generally discerned over the regions such as the western United States and western China, in which the balloon launch times mostly correspond to the afternoon. The diurnal variations in BLH are revealed with a peak at 17:00 local solar time (LST). The most promising reanalysis product is ERA5, which underestimates BLH by around 130 m as compared to radiosondes released during daytime. In addition, MERRA-2 is a well-established product and has an underestimation of around 160 m. JRA-55 and NCEP-2 might produce considerable additional uncertainties, with a much larger underestimation of up to 400 m. The largest bias in the reanalysis data appears over the western United States and western China, and it might be attributed to the maximal BLH in the afternoon when the PBL has risen. Statistical analyses further indicate that the biases of reanalysis BLH products are positively associated with orographic complexity, as well as the occurrence of static instability. To our best knowledge, this study presents the first near-global view of high-resolution radiosonde-derived boundary layer height and provides a quantitative assessment of the four frequently used reanalysis products.

Wind retrieval parameters, i.e. quality indicators and the two-dimensional variational ambiguity removal (2DVAR) analysis speeds, are explored with the aim to improve wind speed retrieval during rain for tropical regions. We apply the well-researched support vector machine (SVM) method in machine learning (ML) to solve this complex problem in a data-oriented regression. To guarantee the effectiveness of SVM, the inputs are extensively analysed to evaluate their appropriateness for this problem, before the results are produced. The comparisons between distributions and differences between data of rain-contaminated winds, corrected winds and good quality C-band winds illustrate that the rain-distorted wind distributions become more nominal with SVM, hence much reducing the rain-induced biases and error variance. Further confirmation is obtained from a case with synchronous Himawari-8 observation indicating rain (clouds) in the scene. Furthermore, the estimation of simultaneous rain rate is attempted with some success to retrieve both wind and rain. Although additional observations or higher resolution may be required to better assess the accuracy of the wind and rain retrievals, the ML results demonstrate benefits of such methodology in geophysical retrieval and nowcasting applications.

The current data paper aims to interlink the African plant collections of the Meise Botanic Garden Herbarium (BR) and the Royal Museum for Central Africa Xylarium (Tw). Complementing both collections strengthens the reference value of each institutional collection, as more complete metadata are made available, and it enables an increased quality control on the identification of wood specimens. Furthermore, the renewed connection enables the linking of available wood trait data with data on phenology, leaf morphology or even molecular information for many tree species, allowing assessments of performance of individual trees. In addition to studies at the interspecific level, comparisons at the intraspecific level become possible, which could lead to important new insights into resilience to and impact of global change, as well as biodiversity conservation or forest management of Central African forest ecosystems.By interlinking the Tervuren Xylarium Wood database with the recently digitised herbarium of Meise Botanic Garden, we were able to establish a link between 6,621 xylarium and 9,641 herbarium records for 6,953 plant specimens. Both institutional databases were complemented with reliable specimen metadata. The Tervuren xylarium now profits from taxonomic revisions made by botanists at Meise BG, and a list of phenotypical features for woody African species can be extended with wood anatomical descriptors. New metadata from the Tw xylarium records were used to add the country of collection to 50 linked BR herbarium specimens for which this information was missing. Furthermore, metadata available from the labels on digitised BR herbarium specimens was used to update Tw xylarium records with the date of collection (817 records), collection locality (698 records), coordinates (482 records) and altitude (817 records). In conclusion, we created a reference database with reliable botanic identities which can be used in a range of studies, such as modelling analyses, community assessments or trait analyses, all framed in a spatiotemporal context. Furthermore, the linked collections hold historical reference data and specimens that can be studied in the context of global changes.

FY-3E WindRAD (Fengyun-3E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The diversity of the radar view geometry varies across the swaths, with maximum diversity in the sweet swaths and limited diversity in the outer and nadir swaths. When NOC backscatter calibration coefficients are computed as a function of incidence angle only (NOCint), a smooth correction is found. However, when relative antenna azimuth angle is included (NOCant), it appears that the corrections as a function of relative azimuth angle vary harmonically and substantially for a specific incidence angle. NOCant corrections yield a better fit of the measurements to the GMF (Geophysical Model Function). Hence, NOCant is applied for the analysis of wind retrieval from the Ku-band and C-band. An extra engineering correction of 0.15 dB and 0.20 dB is applied on Ku-band and C-band backscatter values, respectively, to reduce the wind speed bias without increasing the standard deviation. Overall, NOCant is the best option for both channels. In addition, the instrument backscatter data stability over time is good, and the retrieved winds can fulfill operational requirements.

Aeolus is the first satellite mission to directly observe wind profile information on a global scale. After implementing a set of bias corrections, the Aeolus data products went public on 12 May 2020. However, Aeolus wind products over China have thus far not been evaluated extensively by ground-based remote sensing measurements. In this study, the Mie-cloudy and Rayleigh-clear wind products from Aeolus measurements are validated against wind observations from the radar wind profiler (RWP) network in China. Based on the position of each RWP site relative to the closest Aeolus ground tracks, three matchup categories are proposed, and comparisons between Aeolus wind products and RWP wind observations are performed for each category separately. The performance of Mie-cloudy wind products does not change much between the three matchup categories. On the other hand, for Rayleigh-clear and RWP wind products, categories 1 and 2 are found to have much smaller differences compared with category 3. This could be due to the RWP site being sufficiently approximate to the Aeolus ground track for categories 1 and 2. In the vertical, the Aeolus wind products are similar to the RWP wind observations, except for the Rayleigh-clear winds in the height range of 0–1 km. The mean absolute normalized differences between the Mie-cloudy (Rayleigh-clear) and the RWP wind components are 3.06 (5.45), 2.79 (4.81), and 3.32 (5.72) m/s at all orbit times and ascending and descending Aeolus orbit times, respectively. This indicates that the wind products for ascending orbits are slightly superior to those for descending orbits, and the observation time has a minor effect on the comparison. From the perspective of spatial differences, the Aeolus Mie-cloudy winds are consistent with RWP winds in most of east China, except in coastal areas where the Aeolus Rayleigh-clear winds are more reliable. Overall, the correlation coefficient R between the Mie-cloudy (Rayleigh-clear) wind and RWP wind component observation is 0.94 (0.81), suggesting that Aeolus wind products are in good agreement with wind observations from the RWP network in China. The findings give us sufficient confidence in assimilating the newly released Aeolus wind products in operational weather forecasting in China.

The Chinese HY-2C satellite was launched on Sep. 21, 2020, carrying the new Ku-band scatterometer (HSCAT-C). Different from the other currently operating scatterometers, the HSCAT-C is in a nonsun-synchronous orbit and as such it will be useful for the cross-calibration of sea surface wind products from current space-borne scatterometers and radiometers. In this article, the HSCAT-C wind products are validated by comparing to collocated winds from buoys, European Centre for Medium-Range Weather Forecasts model, and several other scatterometers. The results show that the quality of HSCAT-C winds is very good: in comparison with buoy winds, the wind speed standard deviation and direction root-mean-square error are 1.03 m/s and 15.9°, respectively. The HSCAT-C winds show very good agreements with the HSCAT-B winds, especially in the range of [4, 17] m/s. In addition, as HSCAT-C is a rotating pencil-beam scatterometer, some common error characteristics are also clearly found in the wind products. Based on the validation results and existing scatterometer application experience, it is believed that the availability of the HSCAT-C wind product would greatly benefit the scientific and user community.

In this paper, we deconstruct how geography is organized as an academic study in the Netherlands. We consider how human and physical foci in geography are included in undergraduate and graduate curricula. As a country with a long academic history and renowned geography programmes, the minimal integration between human and physical geography that we identified is remarkable. We reflect on one high-profile societal debate to illustrate the limitations of the current way of conceptualizing geography as a degree programme: the case of human-induced earthquakes in the Dutch province of Groningen due to gas extraction. We argue that countries with academic geography education similar to the Netherlands stand to gain from embracing the integrative aspects to the field of geography. This is particularly the case when considering processes of global change that rapidly and, arguably, increasingly influence socio-spatial inequalities and livelihoods from global to local levels.

Global navigation satellite system reflectometry (GNSS-R)-derived winds from the cyclone GNSS (CYGNSS) satellite constellation are expected to significantly improve weather forecasts in the tropical region. Delay-Doppler maps (DDMs) acquired by the TechDemosat-1 (TDS-1) GNSS-R satellite mission suffer from distortions that are highly correlated to on-board specular point estimation inaccuracies. Such distortions may affect wind retrievals, especially when multilook approaches aiming at exploiting the ambiguity-free area of the DDM are applied. This article demonstrates: that CYGNSS DDMs are also affected by such distortions; the rationale of DDM shape asymmetries induced by specular point location inaccuracies; and a simple strategy for reducing such induced distortions. Two different datasets have been used, consisting of both regular and raw intermediate frequency CYGNSS measurements. The results show that, similar to TDS-1, the CYGNSS DDM distortions are correlated to specular point location inaccuracies. Furthermore, such inaccuracies are significantly reduced if more accurate specular point related parameters are used to recompress the raw GNSS-R echo, highlighting some sampling issues that are common to both TDS-1 and CYGNSS missions. These results suggest that multilook wind retrieval approaches aiming at exploiting also the peripheral parts of the DDM may be seriously compromised by such distortions. The latter may be substantially reduced by oversampling the outcoming DDM and by a posteriori choosing the proper DDM subsample. For future upcoming GNSS-R missions, it is strongly recommended to store the raw data for eventual reprocessing in case of miscalibration or processing issues such as those shown in this article.

Ocean surface rain rate information is crucial for quality control (QC) research in wind scatterometry. High-quality precipitation retrievals from microwave instruments are available from the Global Precipitation Mission (GPM). In addition, rain rates can be estimated with high spatiotemporal resolution from geostationary passive visible-infrared imagers such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites. The two products are complementary in observing time and regions. We compare them at different spatial scales, and show that the best consistency is obtained for grids with a size of 25 km or larger; where 25 km corresponds to a common scatterometer wind vector cell size. The results show that correlation coefficient of rain rates from GPM and MSG products for rain rates less than 5 mm/h is about 0.2 and ranges from 0.2 to about 0.5 for rain rates higher than 5 mm/h, while bias and root mean square deviation fluctuate about 2 and 3 mm/h, respectively. Also, QC indicator performances are analyzed with references to MSG and GPM rain rates respectively, for tropical regions. References to these QC indicators further indicate better consistency with both products at rain rates higher than 5 mm/h. The effectiveness of a newly proposed QC indicator has been confirmed as well. The three-way comparison of rain products and scatterometer QC indicators provides a reliable reference for research and for the application of corrections for rain effects on scatterometers.

Abstract Adaptive evolution is a major driver of organism diversification, but the links between phenotypic traits and environmental niche remain little documented in tropical trees. Moreover, trait‐niche relationships are complex because a correlation between the traits and environmental niches displayed by a sample of species may result from (a) convergent evolution if different environmental conditions have selected different sets of traits, and/or (b) phylogenetic inertia if niche and morphological differences between species are simply function of their phylogenetic divergence, in which case the trait‐niche correlation does not imply any direct causal link. Here, we aim to assess the respective roles of phylogenetic inertia and convergent evolution in shaping the differences of botanical traits and environmental niches among congeneric African tree species that evolved in different biomes. This issue was addressed with the tree genus Guibourtia Benn. (Leguminosae and Detarioideae), which contains 13 African species occupying various forest habitat types, from rain forest to dry woodlands, with different climate and soil conditions. To this end, we combined morphological data with ecological niche modelling and used a highly resolved plastid phylogeny of the 13 African Guibourtia species. First, we demonstrated phylogenetic signals in both morphological traits (Mantel test between phylogenetic and morphological distances between species: r = .24, p = .031) and environmental niches (Mantel test between phylogenetic and niche distances between species: r = .23, p = .025). Second, we found a significant correlation between morphology and niche, at least between some of their respective dimensions (Mantel's r = .32, p = .013), even after accounting for phylogenetic inertia (Phylogenetic Independent Contrast: r = .69, p = .018). This correlation occurred between some leaflet and flower traits and solar radiation, relative humidity, precipitations, and temperature range. Our results demonstrate the convergent evolution of some morphological traits in response to climatic factors in congeneric tree species and highlight the action of selective forces, along with neutral ones, in shaping the divergence between congeneric tropical plants.

Abstract The rotating fan‐beam architecture wind scatterometer Chinese‐French Oceanography Satellite (CSCAT) onboard the Chinese‐French Oceanography Satellite leads to varying geometries and varying wind retrieval performance across the swath. To assess this, the wind vector cells (WVCs) are classified into three groups: outer, sweet, and nadir. The sweet swath WVCs contain the most diverse geometries, which give the best wind retrieval performance, whereas on the opposite the outer swath WVCs have the least diverse geometries, which makes the wind retrieval and ambiguity removal more challenging. NWP Ocean Calibration (NOC) is used to calibrate the radar backscatter prior to the wind inversion. It computes the difference between measured backscatter and simulated backscatter from collocated NWP winds through the geophysical model function. The difference is applied as a correction. An improved NOC method, NOC as a function of incidence angle and azimuth angle (NOCant) is introduced, which takes the orientation of the antenna into account. This method is compared with the widely used NOC as a function of incidence angle only (NOCinc). It shows a better fit with the geophysical model function, except for the outer swath WVCs which have limited geometry diversity. It also corrects the asymmetric wind direction bias distribution across the swath and reduced the nadir swath relative wind direction bias which is caused by the poor azimuth diversity. In conclusion, the rotating fan‐beam architecture of CSCAT leads to unique and varying data characteristics across the swath. Overall, the performance of the proposed NOCant correction is better than NOCinc and improves the wind statistics.

Wind speed reconciliation across different wind sources is critically needed for extending available satellite wind records in Tropical Cyclones. The deviations between wind references of extremes, such as the moored buoy data and dropsonde wind estimates for guidance on geophysical model function development, are one of the main causes of wind speed differences for wind products, for instance, the overestimation of Synthetic Aperture Radars (SARs) relative to ASCAT winds. The study proposes a new wind speed adjustment to achieve mutual adjustment between ASCAT CMOD7 winds and simultaneous SAR wind speeds. The so-called CMOD7D-v2 adjustment is constructed based on the statistical analysis of SAR and ASCAT Tropical Cyclone acquisitions between 2016 and 2021, showing a satisfactory performance in wind speed reconciliation for winds with speeds higher than 14 m/s. Furthermore, the error characteristics of the CMOD7D-v2 adjustment for Tropical Cyclone winds are analyzed using the Triple Collocation analysis technique. The analysis results show that the proposed wind adjustment can reduce ASCAT wind errors by around 16.0% when adjusting ASCAT winds to SAR wind speeds. In particular, when downscaling SAR winds, the improvement in ASCAT wind errors can be up to 42.3%, effectively alleviating wind speed differences across wind sources. Furthermore, to avoid the impacts of large footprints by ASCAT sensors, wind speeds retrieved from SAR VV signals (acting as a substitute for ASCAT winds) are adjusted accordingly and compared against SAR dual-polarized winds and collocated Stepped Frequency Microwave Radiometer (SFMR) observations. We find that the bias values of adjusted winds are lower than products from other adjustment schemes by around 5 m/s at the most extreme values. These promising results verify the plausibility of the CMOD7D-v2 adjustment, which is conducive to SAR and ASCAT wind speed comparisons and extreme wind analysis in Tropical Cyclone cases.

The normalized standard deviation (Kp) of the noise that affects scatterometer Normalized Radar Cross-Sections (σ0s) plays a key role in the ocean and more in particular coastal wind retrieval procedures and the a posteriori quality control. This paper presents a method based on SeaWinds measurements to estimate Kps. The method computes the standard deviation of the differences between the full-resolution (slice) σ0s and the footprint (egg) σ0. The results are compared to the median of Kps provided with SeaWinds σ0s, showing some non-negligible differences. Kps estimated on non-homogeneous surfaces are larger than those estimated on sea, whereas no differences are appreciated in the provided Kps, which is likely due to the ability of this methodology to account for the effect of the scene variability in the estimates. The presence of inter-slice biases is demonstrated with a trend with the antenna azimuth angle. A multi-collocation slice cross-calibration procedure is suggested for the retrieval stage. Finally, a theoretical model of the distribution of σ0s is proposed and used to validate Kps. The results prove the efficacy of this model and that the provided Kps seem to be largely underestimated at low-wind regimes.

The accuracy and consistency of a quintuple collocation analysis of ocean surface vector winds from buoys, scatterometers, and NWP forecasts is established. A new solution method is introduced for the general multiple collocation problem formulated in terms of covariance equations. By a logarithmic transformation, the covariance equations reduce to ordinary linear equations that can be handled using standard methods. The method can be applied to each determined or overdetermined subset of the covariance equations. Representativeness errors are estimated from differences in spatial variances. The results are in good agreement with those from quadruple collocation analyses reported elsewhere. The geometric mean of all solutions from determined subsets of the covariance equations equals the least-squares solution of all equations. The accuracy of the solutions is estimated from synthetic data sets with random Gaussian errors that are constructed from the buoy data using the values of the calibration coefficients and error variances from the quintuple collocation analysis. For the calibration coefficients, the spread in the models is smaller than the accuracy, but for the observation error variances, the spread and the accuracy are about equal only for representativeness errors evaluated at a scale of 200 km for u and 100 km for v. Some average error covariances differ significantly from zero, indicating weak inconsistencies in the underlying error model. Possible causes for this are discussed. With a data set of 2454 collocations, the accuracy in the observation error standard deviation is 0.02 to 0.03 m/s at the one-sigma level for all observing systems.

Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT (China France Oceanography SATellite), WindRad (Chinese Wind Radar) on FY-3E, and SeaWinds on QuikSCAT. Besides the comparison of the so-called first rank solution retrieval skill of the input wind field, other figures of merit (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the wind vector cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir but also to investigate resolution and accuracy through incorporating and analyzing the spatial response functions of the simulated Level-1B data for each WVC.

This paper analyzes the differences between ERA-Interim and ERA5 surface winds fields relative to Advanced Scatterometer (ASCAT) ocean vector wind observations, after adjustment for the effects of atmospheric stability and density, using stress-equivalent winds (U10S) and air–sea relative motion using ocean current velocities. In terms of instantaneous root mean square (rms) wind speed agreement, ERA5 winds show a 20 % improvement relative to ERA-Interim and a performance similar to that of currently operational ECMWF forecasts. ERA5 also performs better than ERA-Interim in terms of mean and transient wind errors, wind divergence and wind stress curl biases. Yet, both ERA products show systematic errors in the partition of the wind kinetic energy into zonal and meridional, mean and transient components. ERA winds are characterized by excessive mean zonal winds (westerlies) with too-weak mean poleward flows in the midlatitudes and too-weak mean meridional winds (trades) in the tropics. ERA stress curl is too cyclonic in midlatitudes and high latitudes, with implications for Ekman upwelling estimates, and lacks detail in the representation of sea surface temperature (SST) gradient effects (along the equatorial cold tongues and Western Boundary Current (WBC) jets) and mesoscale convective airflows (along the Intertropical Convergence Zone and the warm flanks for the WBC jets). It is conjectured that large-scale mean wind biases in ERA are related to their lack of high-frequency (transient wind) variability, which should be promoting residual meridional circulations in the Ferrel and Hadley cells.

Triple collocation analysis is an established technique for calculating the relative linear intercalibration coefficients and observation error variances for physical quantities measured simultaneously in space and time by three different observation systems. A simple parameterized error model is used. It relies on a few assumptions, one of which is that the observation errors are independent of the magnitude of the observed quantities. This is referred to as error orthogonality. Using an ocean surface vector winds data set of 44,948 collocations, this study shows that the violation of error orthogonality does affect the calibration coefficients but has only a small second-order effect on the observation error variances of the calibrated data.

This paper describes the adaptation of the Bayesian sea ice detection algorithm for the rotating fan-beam scatterometer CSCAT onboard the China–France Oceanography Satellite (CFOSAT). The algorithm was originally developed and applied for fixed fan-beam and rotating pencil-beam scatterometers. It is based on the probability of the wind and ice backscatter distances from the measurements to their corresponding geophysical model functions (GMFs). The new rotating Ku-band fan-beam design introduces very diverse geometry distributions across the swath, which leads to three main adaptations of the algorithm: (1) a new probability distribution function fit for the backscatter distances over open sea; (2) a linear ice GMF as a function of incidence angle; (3) the separation of outer swath wind vector cells ((WVCs) number 1, 2, 41, 42) from the other WVCs to form two sets of probability distribution function fits for these two WVC groups. The results are validated against sea ice extents from the active microwave ASCAT and the passive microwave SSMI. The validation shows good agreement with both instruments, despite the discrepancies with SSMI during the melting season, and this discrepancy is caused by the lower sensitivity of the passive microwave to detect the ice at a low concentration with a mixed water/ice state, while the scatterometer is more tolerant regarding this situation. We observed that the sea-ice GMF regression between HH and VV sea-ice backscatter at low and high incidence angles decorrelates at around −12 dB (28) and −20 dB (50) and an experiment with truncated backscatter values at these incidence angles is executed, which significantly improves the year-long average sea ice extents. In conclusion, the adapted algorithm for CSCAT works effectively and yields consistent sea ice extents compared with active and passive microwave instruments. As such, it can, in principle, contribute to the long-term global scatterometer sea ice record, and as the algorithm was adapted for a rotating fan-beam scatterometer, it also can serve as a guideline for the recently launched, dual-frequency, rotating fan-beam scatterometer WindRAD.

Wild genetic resources and their ability to adapt to environmental change are critically important in light of the projected climate change, while constituting the foundation of agricultural sustainability. To address the expected negative effects of climate change on Robusta coffee trees (Coffea canephora), collecting missions were conducted to explore its current native distribution in Uganda over a broad climatic range. Wild material from seven forests could thus be collected. We used 19 microsatellite (SSR) markers to assess genetic diversity and structure of this material as well as material from two ex-situ collections and a feral population. The Ugandan C. canephora diversity was then positioned relative to the species' global diversity structure. Twenty-two climatic variables were used to explore variations in climatic zones across the sampled forests. Overall, Uganda's native C. canephora diversity differs from other known genetic groups of this species. In northwestern (NW) Uganda, four distinct genetic clusters were distinguished being from Zoka, Budongo, Itwara and Kibale forests A large southern-central (SC) cluster included Malabigambo, Mabira, and Kalangala forest accessions, as well as feral and cultivated accessions, suggesting similarity in genetic origin and strong gene flow between wild and cultivated compartments. We also confirmed the introduction of Congolese varieties into the SC region where most Robusta coffee production takes place. Identified populations occurred in divergent environmental conditions and 12 environmental variables significantly explained 16.3% of the total allelic variation across populations. The substantial genetic variation within and between Ugandan populations with different climatic envelopes might contain adaptive diversity to cope with climate change. The accessions that we collected have substantially enriched the diversity hosted in the Ugandan collections and thus contribute to ex situ conservation of this vital genetic resource. However, there is an urgent need to develop strategies to enhance complementary in-situ conservation of Coffea canephora in native forests in northwestern Uganda.

While coffee beans have been studied for many years, researchers are showing a growing interest in coffee leaves and by-products, but little information is currently available on coffee species other than Coffea arabica and Coffea canephora. The aim of this work was to perform a targeted and untargeted metabolomics study on Coffea arabica, Coffea canephora and Coffea anthonyi. The application of the recent high-resolution mass spectrometry-based metabolomics tools allowed us to gain a clear overview of the main differences among the coffee species. The results showed that the leaves and fruits of Coffea anthonyi had a different metabolite profile when compared to the two other species. In Coffea anthonyi, caffeine levels were found in lower concentrations while caffeoylquinic acid and mangiferin-related compounds were found in higher concentrations. A large number of specialized metabolites can be found in Coffea anthonyi tissues, making this species a valid candidate for innovative healthcare products made with coffee extracts.

Whereas, past research has shown that using environmental arguments to reduce meat intake are unsuccessful in awareness campaigns, popular documentaries might have the potential to successfully change the public awareness of the environmental implications of meat consumption today. This preliminary study aimed to provide first empirical evidence of the potential effects of watching a popular documentary on a less-known environmental topic among a population that are habitual performers of the behavior under discussion. More precisely, the effects of watching “Cowspiracy: The Sustainability Secret” on the awareness of the environmental implications of meat consumption, the attitude toward eating less meat, and the intention to eat less meat in young adults who consume meat on an (almost) daily basis was studied. The potential impact of Cowspiracy was investigated from the Integrated Change Model perspective. Paper-and-pencil questionnaires were administered to N = 47 participants aged between 19 and 32 before and after watching either Cowspiracy (experimental group, n = 26) or Planet Earth (control group, n = 21). Controlling for the influence of predisposing factors (sociodemographic characteristics gender, age, and socioeconomic status), the results show that watching a popular documentary about the environmental impact of meat production (Cowspiracy) can have a significant effect on the awareness of the environmental consequences of meat consumption, the attitude toward eating less meat, and the intention to reduce meat consumption of young (almost) daily meat eaters. However, results should be interpreted with caution, given the preliminary nature of our study.

Air–sea fluxes are greatly enhanced by the winds and vertical exchanges generated by mesoscale convective systems (MCSs). In contrast to global numerical weather prediction models, space-borne scatterometers are able to resolve the small-scale wind variability in and near MCSs at the ocean surface. Downbursts of heavy rain in MCSs produce strong gusts and large divergence and vorticity in surface winds. In this paper, 12.5 km wind fields from the ASCAT-A and ASCAT-B tandem mission, collocated with short time series of Meteosat Second Generation 3 km rain fields, are used to quantify correlations between wind divergence and rain in the Inter-Tropical Convergence Zone (ITCZ) of the Atlantic Ocean. We show that when there is extreme rain, there is extreme convergence/divergence in the vicinity. Probability distributions for wind divergence and rain rates were found to be heavy-tailed: exponential tails for wind divergence (P∼e−αδ with slopes that flatten with increasing rain rate), and power-law tails for rain rates (P∼(R*)−β with a slower and approximately equal decay for the extremes of convergence and divergence). Co-occurring points are tabulated in two-by-two contingency tables from which cross-correlations are calculated in terms of the odds and odds ratio for each time lag in the collocation. The odds ratio for extreme convergence and extreme divergence both have a well-defined peak. The divergence time lag is close to zero, while it is 30 min for the convergence peak, implying that extreme rain generally appears after (lags) extreme convergence. The temporal scale of moist convection is thus determined by the slower updraft process, as expected. A structural analysis was carried out that demonstrates consistency with the known structure of MCSs. This work demonstrates that (tandem) ASCAT winds are well suited for air–sea exchange studies in moist convection.

This paper presents the first long-term climate data record of sea ice extents and backscatter derived from intercalibrated satellite scatterometer missions (ERS, QuikSCAT and ASCAT) extending from 1992 to the present date (Verhoef et al., 2018). This record provides a valuable independent account of the evolution of Arctic and Antarctic sea ice extents, one that is in excellent agreement with the passive microwave records during the fall and winter months but shows higher sensitivity to lower concentration and melting sea ice during the spring and summer months. The scatterometer record also provides a depiction of sea ice backscatter at C- and Ku-bands, allowing the separation of seasonal and perennial sea ice in the Arctic and further differentiation between second-year (SY) and older multiyear (MY) ice classes, revealing the emergence of SY ice as the dominant perennial ice type after the historical sea ice loss in 2007 and bearing new evidence on the loss of multiyear ice in the Arctic over the last 25 years. The relative good agreement between the backscatter-based sea ice (FY, SY and older MY) classes and the ice thickness record from Cryosat suggests its applicability as a reliable proxy in the historical reconstruction of sea ice thickness in the Arctic.

We present column parity mixers (CPM), a generalization of the Θ mixing layer that is used in Keccak. Thanks to our description using matrix arithmetic, we can easily derive algebraic, diffusion, and mask propagation properties, leading to a surprising distinction between two types of CPMs. We compare CPMs to other popular types of mixing layers and argue that CPMs can be more efficient. While Keccak has a bit-oriented structure, we make the case that CPMs are also suitable for nibble- or byte-oriented designs. We outline a general substitution-permutation-network-based design strategy using a CPM, for which we show how one can attain strong bounds for differential and linear trails. We apply this strategy concretely to design a 256-bit permutation with an efficient inverse and strong trail bounds. Our permutation design uses a number of ideas that are of independent interest and allows a fast bitsliced implementation that compares quite well with other established ciphers and permutations.

Recently a lot of attention is paid to the search for efficiently implementable MDS matrices for lightweight symmetric primitives. Most previous work concentrated on locally optimizing the multiplication with single matrix elements. Separate from this line of work, several heuristics were developed to find shortest linear straightline programs. Solving this problem actually corresponds to globally optimizing multiplications by matrices. In this work we combine those, so far largely independent lines of work. As a result, we achieve implementations of known, locally optimized, and new MDS matrices that significantly outperform all implementations from the literature. Interestingly, almost all previous locally optimized constructions behave very similar with respect to the globally optimized implementation. As a side effect, our work reveals the so far best implementation of the Aes Mix- Columns operation with respect to the number of XOR operations needed.