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Figures 9–11 in A new paracolletine bee from Colombia (Hymenoptera: Colletidae), with an updated checklist of the tropical Andean bee fauna

Figures 9–11. Male paratype of Lonchopria (Biglossa) comforti, new species. 9. Lateral habitus. 10. Dorsal habitus. 11. Facial view.

Figures 3–5 in A new paracolletine bee from Colombia (Hymenoptera: Colletidae), with an updated checklist of the tropical Andean bee fauna

Figures 3–5. Female holotype of Lonchopria (Biglossa) comforti, new species (except paratype in figure 4). 3. Lower three-quarters of head detailing clypeal structure and sculpturing. 4. Outer surface of mandible. 5. Full facial view.

Figures 6–8 in A new paracolletine bee from Colombia (Hymenoptera: Colletidae), with an updated checklist of the tropical Andean bee fauna

Figures 6–8. Female holotype of Lonchopria (Biglossa) comforti, new species (except paratype in fig- ure 6). 6. Left half of mesoscutum and mesoscutellum. 7. Metatibia and metabasitarsus. 8. Detail of metasomal terga.

Figures 1–2 in A new paracolletine bee from Colombia (Hymenoptera: Colletidae), with an updated checklist of the tropical Andean bee fauna

Figures 1–2. Female holotype of Lonchopria (Biglossa) comforti, new species. 1. Lateral habitus. 2. Dorsal habitus.

Underwater ambient sound in tropical cyclones

Underwater ambient sound measurements were made in three tropical cyclones: Hurricane Gustav (2008) in the Gulf of Mexico, and Typhoons Fanapi (September 2010) and Megi (October 2010) in the western Pacific Ocean as part of the ITOP (Impact of Typhoons on the Ocean in the Pacific) program.  Measurements were made from eight Lagrangian floats, each equipped with one hydrophone, air deployed ahead of these storms by WC-130J aircraft operated by the U.S. Air Force Reserve 53rd Weather Reconnaissance squadron Hurricane Hunters.  Floats 50 and 51 were in Gustav, 60, 61 and 62 in Fanapi, and 66, 67 and 68 in Megi. After the storm passage, the Lagrangian floats were recovered by a research vessel.  Float positions were determined by interpolating between a few GPS positions taken during the storm passage guided by time-integrated velocity measurements from Electromagnetic Autonomous Profiling Explorer (EM-APEX) floats deployed at the same time. During the passage of tropical cyclones, the hydrophone switched between the work and sleep modes every 30 minutes due to limited data storage.  In the work mode, the hydrophones sampled underwater ambient sound twice per second.  The sound measurements thus are in 30-min segments.  There are 39 (50), 38 (51), 60 (60), 56 (61), 56 (62), 55 (66), 53 (67) and 51 (68) segments (float serial numbers are in brackets), respectively, giving a total of 408 data segments and about 190 hours of sound measurements.  Each raw time series has been Fourier transformed to obtain a power spectrum from 40 Hz to 50 kHz, with a spectral resolution of 40 Hz.  The sound pressure level (SPL) in decibels (dB) is defined as \(\textrm{SPL} = 20\cdot \textrm{log}(\textrm{P}/\textrm{P}_\textrm{ref}),\) where \(\textrm{P}\) is the hydrophone measured sound pressure, and \(\textrm{P}_\textrm{ref}\) is the reference pressure 1 \(\mu \textrm{Pa}^{2} \textrm{Hz}^{-2}\).  The hydrophones were inter-calibrated in laboratory before and after the deployments, and agreed with a root-mean-square difference of 1–2 dB. The raw sound measurements are labeled SpdbP_raw. Each Lagrangian float carried a variety of instruments including a pumped CTD (conductivity, temperature and depth) sensor, a pumped GTD (gas tension device), a motor to control drogue, and another motor to control the float's buoyancy.  These instruments generated noise of different temporal and spectral features.  Sound measurements contaminated by noise were removed as described in Zhao et al. (2014 JPO). One exception is GTD, which ran for 90% of the time for floats 50 and 51 (Gustav) and 66, 67, and 68 (Megi), and caused significant contamination on the < 5-kHz sound data.  However, the > 5-kHz sound measurements are not affected by the GTD noise, and thus kept for future studies (detect rain events and breaking waves). The cleaned sound measurements are labeled SpdbP_clean. We decomposed the underwater ambient sound into three components according to their time scales.  First, we calculate background sound, defined as the mean of the lowest 10% sound level over the 30 min period.  The background sound generally rises and falls with increasing/decreasing wind speed and the presence of bubble clouds.  Second, sound fluctuation is obtained by removing the background sound from the original data. Third, the sound fluctuation is divided into two components using two matched temporal filters. The second-scale fluctuation is obtained by high-pass filtering the sound fluctuation using 20-second running mean.  The minute-scale fluctuation is obtained by low-pass filtering the sound fluctuation. By this method, the original underwater ambient sound is decomposed into three components: background (SpdbP_background), minute-scale fluctuation (SpdbP_MidFreq), and second-scale fluctuation (SpdbP_HighFreq).  We applied the above decomposition method to all 408 30-min sound segments from eight Lagrangian floats, and created 408 figures with the same format. We share here the raw, de-noised, and decomposed sound data for all eight Lagrangian floats (eight Matlab data files) and demonstrate their decomposed sound data (eight PDF files). <a href="/api/files/d9886aea-3829-43d2-b10e-d57bdd77ae7a/Fig-5-Q101623.pdf?versionId=fee16fdd-adef-4419-98f7-3f05188065dd">Fig-5-Q101623.pdf </a> and <a href="/api/files/d9886aea-3829-43d2-b10e-d57bdd77ae7a/Fig-6-J091801.pdf?versionId=1607d29d-cb3c-4cbb-bb58-12c09b3a788c">Fig-6-J091801.pdf </a>are Figures 5 and 6 in a recent paper (<a href="https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0078.1/JTECH-D-22-0078.1.xml">https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0078.1/JTECH-D-22-0078.1.xml</a>).

Supporting Dataset for "Reducing a tropical cyclone weak-intensity bias in a global numerical weather prediction system"

This archive supports the submission of "Reducing a tropical cyclone weak intensity bias in a global numerical weather prediction system" to Monthly Weather Review.  It contains model configurations, the software used to create ensemble perturbations, the software used to compute the diagnostics discussed in the text, and the software use to plot figures. After downloading, the contents can be extracted using: <blockquote> tar -xzf idealtc1_archive-1.tgz </blockquote>

Soil physical quality of a tropical sandy Ferralsol

Dados de pesquisa da dissertação de mestrado do primeiro autor, vinculado à Universidade Estadual de Maringá, Maringá, BR.

Fig. 7 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot

Fig. 7. Dibolostethus kattani Means, Bouzan, Martínez-Torres & Ivanov sp. nov., holotype, ♂ (ICN- MD-1317A), right gonopod. A. Mesal view. B. Anterior view. C. Lateral view. Red circles = cingulum. Dashed lines = prostatic groove. Abbreviations: Lb = lobe; PfP = prefemoral process; S = solenomere; Sh = shelf.

Fig. 5. Dibolostethus sicarius Hoffman, 2009 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot

Fig. 5. Dibolostethus sicarius Hoffman, 2009, holotype, ♂ (VMNH110810), left gonopod. A. Lateral view. B. Anterior view. C. Mesal view. Red circles = cingulum. Dashed lines = prostatic groove. Abbreviations: PfP = prefemoral process; S = solenomere.

Fig. 4. Body ring 4, sternal projections. A–B. Dibolostethus sicarius Hoffman, 2009 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot

Fig. 4. Body ring 4, sternal projections. A–B. Dibolostethus sicarius Hoffman, 2009, holotype, ♂ (VMNH110810). C–D. D. inopinatus Means, Bouzan & Ivanov sp. nov., holotype, ♂ (VMNH110813). A, C. Lateral views. B, D. Posterolateral views. Abbreviation: Cx = coxae.

Fig. 3. Dibolostethus Hoffman, 2009 somatic characters continued. A, E. Dibolostethus sicarius Hoffman, 2009 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot

Fig. 3. Dibolostethus Hoffman, 2009 somatic characters continued. A, E. Dibolostethus sicarius Hoffman, 2009, holotype, ♂ (VMNH110810). B–D. D. kattani Means, Bouzan, Martínez-Torres & Ivanov sp. nov., holotype, ♂ (ICN-MD-1317-1). A. Coxa 2, mesal view, red arrow = gonopore. B. Left leg 4, posterior view, red arrow = tibial ventro-apical projection. C. Left leg of 5th leg pair showing incrassate femur, red arrow = tibial ventro-apical projection. D. Body ring 5, lateral view, red circle = anterior pair of sternal projections. E. Prefemur 5, red arrow = basal pore. Abbreviations: Cx = coxa; Pf = prefemur.

Fig. 1 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot

Fig. 1. Dibolostethus kattani Means, Bouzan, Martínez-Torres & Ivanov sp. nov., ♂ (ICN-MD-1367), habitus. Scale bar = 5 mm.

Fig. 9 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot

Fig. 9. Distribution of Chelodesmidae spp. in the Tropical Andes Biodiversity Hotspot, by country. Note: the type locality of Dibolostethus sicarius Hoffman, 2009 is included for completeness although it falls just outside of the boundaries of the Tropical Andes Biodiversity Hotspot. For information on these taxa see Supp. file 1.

Fig. 2 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot

Fig. 2. Scanning electron microscope images of Dibolostethus Hoffman, 2009 showing somatic characters. Dibolostethus kattani Means, Bouzan, Martínez-Torres & Ivanov sp. nov., paratype, ♂ (ICN- MD-1317B). A. Body rings 9th and 10th, red rectangle = median metazonal sulcus. B. Venter of 4th body ring, showing paired sternal projections, red oval = stigma. C. Venter of head, showing narrow gnathochilarium. D. Tip of seventh antennomere, red arrow = narrow slit connecting pocket containing sensilla basiconica and sensory cone area.

Fig. 6 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot

Fig. 6. Dibolostethus inopinatus Means, Bouzan & Ivanov sp. nov., holotype, ♂ (VMNH110813), left gonopod. A. Lateral view, red circle = cingulum. B. Anterior view. C. Mesal view. Dashed lines = prostatic groove. Abbreviations: PfP = prefemoral process; S = solenomere.

Fig. 8. Dibolostethus sicarius Hoffman, 2009 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot

Fig. 8. Dibolostethus sicarius Hoffman, 2009, paratype, ♀ (VMNH110811), vulvae. A. Left vulva, lateroventral view, with operculum at base. B. Vulvae in situ, held appressed to 2nd leg pair coxae with valve openings facing each other. C. Left vulva, lateral view.

A new Approach to derive Productivity of Tropical Forests using Radar Remote

Supplemental material to the publication " A new Approach to derive Productivity of Tropical Forests using Radar Remote Sensing Measurements" in Royal Society Open ScienceAbstract:Deriving gross & net primary productivity (GPP & NPP) andcarbon turnover time of forests from remote sensing remainschallenging. This study presents a novel approach to estimateforest productivity by combining radar remote sensingmeasurements, machine learning and an individual-basedforest model. In this study, we analyse the role of differentspatial resolutions on predictions in the context of the RadarBIOMASS mission (by ESA). In our analysis, we use the forestgap model FORMIND in combination with a boostedregression tree (BRT) to explore how spatial biomassdistributions can be used to predict GPP, NPP and carbonturnover time (τ) at different resolutions. We simulatedifferent spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) incombination with different vertical resolutions (20, 10 and 2m). Additionally, we analysed the robustness of this approachand applied it to disturbed and mature forests. Disturbedforests have a strong influence on the predictions which leadsto high correlations (R2> 0.8) at the spatial scale of 4 ha and 1ha. Increased vertical resolution leads generally to betterpredictions for productivity (GPP & NPP). Increasing spatialresolution leads to better predictions for mature forests andlower correlations for disturbed forests. Our results emphasizethe value of the forthcoming BIOMASS satellite missionand highlight the potential of deriving estimates for forestproductivity from information on forest structure. If appliedto more and larger areas, the approach might ultimatelycontribute to a better understanding of forest ecosystems.

Data for "Tropical Cyclone Supercell Response to the Coast using a Climatology of Radar-Derived Azimuthal Shear"

This dataset contains a .csv file published alongside the article entitled "Tropical Cyclone Supercell Response to the Coast using a Climatology of Radar-Derived Azimuthal Shear" for consideration in Geophysical Research Letters.

Reduction in the Tropical High Cloud Fraction in Response to an Indirect Weakening of the Hadley Cell

NetCDF files for figures in "Reduction in the Tropical High Cloud Fraction in Response to an Indirect Weakening of the Hadley Cell" submitted to Journal of Advances in Modeling Earth Systems. Figure 1: Prescribed SST for control and perturbed simulations:  <ul> <li>sst_ctrl_annualzonalmeans.nc</li> <li>sst_pert_annualzonalmeans.nc</li> </ul> Figure 2: Mass meridional stream function data for control and perturbed simulations: Figure 2a: PC2 cloud scheme + Gregory-Rowntree mass-flux convection scheme <ul> <li>mmsf_pc2gr_ctrl_annualmeans.nc</li> <li>mmsf_pc2gr_pert_annualmeans.nc</li> </ul> Figure 2b: Smith cloud scheme + Gregory-Rowntree mass-flux convection scheme <ul> <li>mmsf_smithgr_ctrl_annualmeans.nc</li> <li>mmsf_smithgr_pert_annualmeans.nc</li> </ul> Figure 3a: Ice cloud fraction data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>Cf_pc2gr_ctrl_annualzonalmeans.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3b: Ice cloud fraction data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>Cf_smithgr_ctrl_annualzonalmeans.nc</li> <li>Cf_smithgr_pert_annualzonalmeans.nc</li> </ul> Figure 3c: Relative humidity data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>RH_pc2gr_ctrl_annualzonalmeans.nc</li> <li>RH_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3d: Relative humidity for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>RH_smithgr_ctrl_annualzonalmeans.nc</li> <li>RH_smithgr_pert_annualzonalmeans.nc</li> </ul> Figure 3e: Specific humidity data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>q_pc2gr_ctrl_annualzonalmeans.nc</li> <li>q_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3f: Specific humidity data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>q_smithgr_ctrl_annualzonalmeans.nc</li> <li>q_smithgr_pert_annualzonalmeans.nc</li> </ul> Figure 3g: Temperature data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>T_pc2gr_ctrl_annualzonalmeans.nc</li> <li>T_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3h: Temperature data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>T_smithgr_ctrl_annualzonalmeans.nc</li> <li>T_smithgr_pert_annualzonalmeans.nc</li> </ul>  Figure 5: Control and perturbed simulation data for ice cloud fraction and mass fraction of ice cloud condensates meaned over the tropical ascent region for PC2 scheme <ul> <li>Cf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>qcf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>qcf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 6a,b: Monthly-mean data of convective mass flux for control and perturbed simulations over the tropics between \(\pm\) 31 \(^\circ\)N for PC2 cloud scheme <ul> <li>convmassflux_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>convmassflux_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 6c,d: Monthly-mean data of net convective detrainment for control and perturbed simulations over the tropics between \(\pm\) 31 \(^\circ\)N for PC2 cloud scheme <ul> <li>netconvdetrain_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>netconvdetrain_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 6e,f: Monthly-mean data of tendency of ice cloud condensates due to convection for control and perturbed simulations over the tropics between \(\pm\) 31 \(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtconv_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>dqcfdtconv_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 7: Annual and zonal-mean data of net convective detrainment for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>netconvdetrain_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>netconvdetrain_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 8: Annual and zonal-mean data of net convective detrainment and ice cloud fraction for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>netconvdetrain_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>netconvdetrain_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 9: Annual and zonal-mean data of convective tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtconv_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqcfdtconv_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 10: Annual and zonal-mean data of net depositional tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtnetdep_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqcfdtnetdep_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 11: Monthly-mean data of net depositional tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropics between \(\pm\) 31\(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtnetdep_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>dqcfdtnetdep_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 12: Annual and zonal-mean data of specific humidity and advective tendency for specific humidity for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>q_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>q_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqdtadv_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqdtadv_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul> Figure 13: Control and perturbed simulation data for mean vertical advection of moisture, mean vertical moisture convergence, total advection of moisture, and total moisture convergence (meaned over the tropical ascent region for PC2 scheme for 15 simulation years) <ul> <li>MeanVerticalAdvectionOfMoisture_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalAdvectionOfMoisture_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalMoistureConvergence_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalMoistureConvergence_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureAdvection_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureAdvection_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureConvergence_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureConvergence_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> </ul>

Dataset for "Assessing Storm Surge Multi-Scenarios based on Ensemble Tropical Cyclone Forecasting" paper

1000 ensemble track forecast of tropical cyclone Hagibis (2019) is provided in NetCDF format and the computed storm surge forecast is provided in the Excel file.